{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Team</th>\n",
       "      <th>G</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>...</th>\n",
       "      <th>FT%</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>6</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>86.8</td>\n",
       "      <td>0.470</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>0.391</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>26.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>20.2</td>\n",
       "      <td>107.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>6</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>85.2</td>\n",
       "      <td>0.479</td>\n",
       "      <td>6.8</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0.353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.752</td>\n",
       "      <td>11.2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>19.3</td>\n",
       "      <td>106.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.497</td>\n",
       "      <td>13.4</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.879</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>123.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>93.2</td>\n",
       "      <td>0.494</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>0.336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770</td>\n",
       "      <td>13.8</td>\n",
       "      <td>39.0</td>\n",
       "      <td>52.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>122.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.2</td>\n",
       "      <td>87.8</td>\n",
       "      <td>0.458</td>\n",
       "      <td>11.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.814</td>\n",
       "      <td>8.8</td>\n",
       "      <td>29.6</td>\n",
       "      <td>38.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>115.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>38.4</td>\n",
       "      <td>91.4</td>\n",
       "      <td>0.420</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808</td>\n",
       "      <td>11.4</td>\n",
       "      <td>29.2</td>\n",
       "      <td>40.6</td>\n",
       "      <td>19.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>111.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.447</td>\n",
       "      <td>12.8</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788</td>\n",
       "      <td>10.6</td>\n",
       "      <td>34.8</td>\n",
       "      <td>45.4</td>\n",
       "      <td>17.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>14.4</td>\n",
       "      <td>23.4</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>84.2</td>\n",
       "      <td>0.435</td>\n",
       "      <td>15.4</td>\n",
       "      <td>42.2</td>\n",
       "      <td>0.365</td>\n",
       "      <td>...</td>\n",
       "      <td>0.754</td>\n",
       "      <td>8.6</td>\n",
       "      <td>35.6</td>\n",
       "      <td>44.2</td>\n",
       "      <td>18.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.4</td>\n",
       "      <td>84.4</td>\n",
       "      <td>0.479</td>\n",
       "      <td>12.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.850</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>44.6</td>\n",
       "      <td>26.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10.4</td>\n",
       "      <td>31.4</td>\n",
       "      <td>0.331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756</td>\n",
       "      <td>10.2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>22.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>105.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>86.4</td>\n",
       "      <td>0.400</td>\n",
       "      <td>9.4</td>\n",
       "      <td>35.8</td>\n",
       "      <td>0.263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750</td>\n",
       "      <td>11.8</td>\n",
       "      <td>35.4</td>\n",
       "      <td>47.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>86.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>12.5</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.370</td>\n",
       "      <td>...</td>\n",
       "      <td>0.759</td>\n",
       "      <td>9.8</td>\n",
       "      <td>43.5</td>\n",
       "      <td>53.3</td>\n",
       "      <td>28.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>14.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>121.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.388</td>\n",
       "      <td>10.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>0.299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.775</td>\n",
       "      <td>9.6</td>\n",
       "      <td>33.8</td>\n",
       "      <td>43.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>11.8</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.759</td>\n",
       "      <td>7.5</td>\n",
       "      <td>40.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>20.3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>99.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>96.8</td>\n",
       "      <td>0.388</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.727</td>\n",
       "      <td>10.8</td>\n",
       "      <td>31.5</td>\n",
       "      <td>42.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>24.3</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>0.401</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.720</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>40.3</td>\n",
       "      <td>20.8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rk                    Team  G     MP    FG   FGA    FG%    3P   3PA  \\\n",
       "0    1          Denver Nuggets  6  240.0  40.8  86.8  0.470  11.0  28.2   \n",
       "1    2       San Antonio Spurs  6  240.0  40.8  85.2  0.479   6.8  19.3   \n",
       "2    3   Golden State Warriors  5  240.0  44.2  89.0  0.497  13.4  32.6   \n",
       "3    4      Philadelphia 76ers  5  240.0  46.0  93.2  0.494   9.0  26.8   \n",
       "4    5    Los Angeles Clippers  5  240.0  40.2  87.8  0.458  11.6  31.6   \n",
       "5    6           Brooklyn Nets  5  240.0  38.4  91.4  0.420  11.0  34.0   \n",
       "6    7  Portland Trail Blazers  5  240.0  39.8  89.0  0.447  12.8  31.6   \n",
       "7    8         Houston Rockets  5  240.0  36.6  84.2  0.435  15.4  42.2   \n",
       "8    9         Toronto Raptors  5  240.0  40.4  84.4  0.479  12.0  32.6   \n",
       "9   10   Oklahoma City Thunder  5  240.0  37.8  86.0  0.440  10.4  31.4   \n",
       "10  11               Utah Jazz  5  240.0  34.6  86.4  0.400   9.4  35.8   \n",
       "11  12         Milwaukee Bucks  4  240.0  44.0  86.3  0.510  12.5  33.8   \n",
       "12  13           Orlando Magic  5  240.0  32.2  83.0  0.388  10.4  34.8   \n",
       "13  14          Boston Celtics  4  240.0  35.5  80.0  0.444  11.8  29.5   \n",
       "14  15         Detroit Pistons  4  240.0  37.5  96.8  0.388  11.0  33.3   \n",
       "15  16          Indiana Pacers  4  240.0  33.5  83.5  0.401  10.0  29.8   \n",
       "\n",
       "      3P%  ...      FT%   ORB   DRB   TRB   AST  STL  BLK   TOV    PF    PTS  \n",
       "0   0.391  ...    0.732  10.0  32.5  42.5  26.8  4.8  2.8   9.7  20.2  107.7  \n",
       "1   0.353  ...    0.752  11.2  34.0  45.2  21.5  4.8  3.8   8.8  19.3  106.2  \n",
       "2   0.411  ...    0.879  11.0  35.0  46.0  31.0  7.2  7.2  15.2  26.2  123.6  \n",
       "3   0.336  ...    0.770  13.8  39.0  52.8  27.0  7.8  6.0  16.2  24.2  122.4  \n",
       "4   0.367  ...    0.814   8.8  29.6  38.4  26.0  8.2  3.0  15.0  24.8  115.6  \n",
       "5   0.324  ...    0.808  11.4  29.2  40.6  19.2  7.4  2.8  14.2  22.8  111.4  \n",
       "6   0.405  ...    0.788  10.6  34.8  45.4  17.4  8.0  5.8  14.4  23.4  111.0  \n",
       "7   0.365  ...    0.754   8.6  35.6  44.2  18.4  9.2  7.0  15.2  21.4  107.0  \n",
       "8   0.368  ...    0.850   7.0  37.6  44.6  26.2  8.4  4.2  12.2  23.0  106.4  \n",
       "9   0.331  ...    0.756  10.2  33.0  43.2  22.6  6.6  5.4  15.2  24.2  105.2  \n",
       "10  0.263  ...    0.750  11.8  35.4  47.2  23.0  9.2  5.4  15.2  21.8   97.8  \n",
       "11  0.370  ...    0.759   9.8  43.5  53.3  28.5  5.8  8.8  14.5  20.0  121.8  \n",
       "12  0.299  ...    0.775   9.6  33.8  43.4  19.0  6.6  4.0  15.6  18.8   92.0  \n",
       "13  0.398  ...    0.759   7.5  40.3  47.8  20.3  5.5  3.0  16.0  19.5   99.3  \n",
       "14  0.331  ...    0.727  10.8  31.5  42.3  23.0  7.3  4.0  10.5  24.3   98.0  \n",
       "15  0.336  ...    0.720   8.0  32.3  40.3  20.8  8.3  3.5  12.0  20.0   91.8  \n",
       "\n",
       "[16 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_stats_df = pd.read_csv('data/nba1.txt',sep=',')\n",
    "team_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Team</th>\n",
       "      <th>G</th>\n",
       "      <th>oppMP</th>\n",
       "      <th>oppFG</th>\n",
       "      <th>oppFGA</th>\n",
       "      <th>oppFG%</th>\n",
       "      <th>opp3P</th>\n",
       "      <th>opp3PA</th>\n",
       "      <th>opp3P%</th>\n",
       "      <th>...</th>\n",
       "      <th>oppFT%</th>\n",
       "      <th>oppORB</th>\n",
       "      <th>oppDRB</th>\n",
       "      <th>oppTRB</th>\n",
       "      <th>oppAST</th>\n",
       "      <th>oppSTL</th>\n",
       "      <th>oppBLK</th>\n",
       "      <th>oppTOV</th>\n",
       "      <th>oppPF</th>\n",
       "      <th>oppPTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Boston Celtics</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>0.401</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.8</td>\n",
       "      <td>0.336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.720</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.3</td>\n",
       "      <td>40.3</td>\n",
       "      <td>20.8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Milwaukee Bucks</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>96.8</td>\n",
       "      <td>0.388</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0.331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.727</td>\n",
       "      <td>10.8</td>\n",
       "      <td>31.5</td>\n",
       "      <td>42.3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>24.3</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Indiana Pacers</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>11.8</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.759</td>\n",
       "      <td>7.5</td>\n",
       "      <td>40.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>20.3</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>99.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Toronto Raptors</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.388</td>\n",
       "      <td>10.4</td>\n",
       "      <td>34.8</td>\n",
       "      <td>0.299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.775</td>\n",
       "      <td>9.6</td>\n",
       "      <td>33.8</td>\n",
       "      <td>43.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.8</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Detroit Pistons</td>\n",
       "      <td>4</td>\n",
       "      <td>240.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>86.3</td>\n",
       "      <td>0.510</td>\n",
       "      <td>12.5</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0.370</td>\n",
       "      <td>...</td>\n",
       "      <td>0.759</td>\n",
       "      <td>9.8</td>\n",
       "      <td>43.5</td>\n",
       "      <td>53.3</td>\n",
       "      <td>28.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>14.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>121.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Houston Rockets</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>86.4</td>\n",
       "      <td>0.400</td>\n",
       "      <td>9.4</td>\n",
       "      <td>35.8</td>\n",
       "      <td>0.263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750</td>\n",
       "      <td>11.8</td>\n",
       "      <td>35.4</td>\n",
       "      <td>47.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Portland Trail Blazers</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>37.8</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10.4</td>\n",
       "      <td>31.4</td>\n",
       "      <td>0.331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756</td>\n",
       "      <td>10.2</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>22.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>105.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Orlando Magic</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.4</td>\n",
       "      <td>84.4</td>\n",
       "      <td>0.479</td>\n",
       "      <td>12.0</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.850</td>\n",
       "      <td>7.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>44.6</td>\n",
       "      <td>26.2</td>\n",
       "      <td>8.4</td>\n",
       "      <td>4.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Utah Jazz</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>84.2</td>\n",
       "      <td>0.435</td>\n",
       "      <td>15.4</td>\n",
       "      <td>42.2</td>\n",
       "      <td>0.365</td>\n",
       "      <td>...</td>\n",
       "      <td>0.754</td>\n",
       "      <td>8.6</td>\n",
       "      <td>35.6</td>\n",
       "      <td>44.2</td>\n",
       "      <td>18.4</td>\n",
       "      <td>9.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Oklahoma City Thunder</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>39.8</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.447</td>\n",
       "      <td>12.8</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788</td>\n",
       "      <td>10.6</td>\n",
       "      <td>34.8</td>\n",
       "      <td>45.4</td>\n",
       "      <td>17.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>14.4</td>\n",
       "      <td>23.4</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Philadelphia 76ers</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>38.4</td>\n",
       "      <td>91.4</td>\n",
       "      <td>0.420</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.324</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808</td>\n",
       "      <td>11.4</td>\n",
       "      <td>29.2</td>\n",
       "      <td>40.6</td>\n",
       "      <td>19.2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>111.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Golden State Warriors</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.2</td>\n",
       "      <td>87.8</td>\n",
       "      <td>0.458</td>\n",
       "      <td>11.6</td>\n",
       "      <td>31.6</td>\n",
       "      <td>0.367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.814</td>\n",
       "      <td>8.8</td>\n",
       "      <td>29.6</td>\n",
       "      <td>38.4</td>\n",
       "      <td>26.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>115.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Brooklyn Nets</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>93.2</td>\n",
       "      <td>0.494</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>0.336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.770</td>\n",
       "      <td>13.8</td>\n",
       "      <td>39.0</td>\n",
       "      <td>52.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>122.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Los Angeles Clippers</td>\n",
       "      <td>5</td>\n",
       "      <td>240.0</td>\n",
       "      <td>44.2</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.497</td>\n",
       "      <td>13.4</td>\n",
       "      <td>32.6</td>\n",
       "      <td>0.411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.879</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>26.2</td>\n",
       "      <td>123.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Denver Nuggets</td>\n",
       "      <td>6</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>85.2</td>\n",
       "      <td>0.479</td>\n",
       "      <td>6.8</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0.353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.752</td>\n",
       "      <td>11.2</td>\n",
       "      <td>34.0</td>\n",
       "      <td>45.2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>19.3</td>\n",
       "      <td>106.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>San Antonio Spurs</td>\n",
       "      <td>6</td>\n",
       "      <td>240.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>86.8</td>\n",
       "      <td>0.470</td>\n",
       "      <td>11.0</td>\n",
       "      <td>28.2</td>\n",
       "      <td>0.391</td>\n",
       "      <td>...</td>\n",
       "      <td>0.732</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>26.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>20.2</td>\n",
       "      <td>107.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Rk                    Team  G  oppMP  oppFG  oppFGA  oppFG%  opp3P  \\\n",
       "0    1          Boston Celtics  4  240.0   33.5    83.5   0.401   10.0   \n",
       "1    2         Milwaukee Bucks  4  240.0   37.5    96.8   0.388   11.0   \n",
       "2    3          Indiana Pacers  4  240.0   35.5    80.0   0.444   11.8   \n",
       "3    4         Toronto Raptors  5  240.0   32.2    83.0   0.388   10.4   \n",
       "4    5         Detroit Pistons  4  240.0   44.0    86.3   0.510   12.5   \n",
       "5    6         Houston Rockets  5  240.0   34.6    86.4   0.400    9.4   \n",
       "6    7  Portland Trail Blazers  5  240.0   37.8    86.0   0.440   10.4   \n",
       "7    8           Orlando Magic  5  240.0   40.4    84.4   0.479   12.0   \n",
       "8    9               Utah Jazz  5  240.0   36.6    84.2   0.435   15.4   \n",
       "9   10   Oklahoma City Thunder  5  240.0   39.8    89.0   0.447   12.8   \n",
       "10  11      Philadelphia 76ers  5  240.0   38.4    91.4   0.420   11.0   \n",
       "11  12   Golden State Warriors  5  240.0   40.2    87.8   0.458   11.6   \n",
       "12  13           Brooklyn Nets  5  240.0   46.0    93.2   0.494    9.0   \n",
       "13  14    Los Angeles Clippers  5  240.0   44.2    89.0   0.497   13.4   \n",
       "14  15          Denver Nuggets  6  240.0   40.8    85.2   0.479    6.8   \n",
       "15  16       San Antonio Spurs  6  240.0   40.8    86.8   0.470   11.0   \n",
       "\n",
       "    opp3PA  opp3P%   ...    oppFT%  oppORB  oppDRB  oppTRB  oppAST  oppSTL  \\\n",
       "0     29.8   0.336   ...     0.720     8.0    32.3    40.3    20.8     8.3   \n",
       "1     33.3   0.331   ...     0.727    10.8    31.5    42.3    23.0     7.3   \n",
       "2     29.5   0.398   ...     0.759     7.5    40.3    47.8    20.3     5.5   \n",
       "3     34.8   0.299   ...     0.775     9.6    33.8    43.4    19.0     6.6   \n",
       "4     33.8   0.370   ...     0.759     9.8    43.5    53.3    28.5     5.8   \n",
       "5     35.8   0.263   ...     0.750    11.8    35.4    47.2    23.0     9.2   \n",
       "6     31.4   0.331   ...     0.756    10.2    33.0    43.2    22.6     6.6   \n",
       "7     32.6   0.368   ...     0.850     7.0    37.6    44.6    26.2     8.4   \n",
       "8     42.2   0.365   ...     0.754     8.6    35.6    44.2    18.4     9.2   \n",
       "9     31.6   0.405   ...     0.788    10.6    34.8    45.4    17.4     8.0   \n",
       "10    34.0   0.324   ...     0.808    11.4    29.2    40.6    19.2     7.4   \n",
       "11    31.6   0.367   ...     0.814     8.8    29.6    38.4    26.0     8.2   \n",
       "12    26.8   0.336   ...     0.770    13.8    39.0    52.8    27.0     7.8   \n",
       "13    32.6   0.411   ...     0.879    11.0    35.0    46.0    31.0     7.2   \n",
       "14    19.3   0.353   ...     0.752    11.2    34.0    45.2    21.5     4.8   \n",
       "15    28.2   0.391   ...     0.732    10.0    32.5    42.5    26.8     4.8   \n",
       "\n",
       "    oppBLK  oppTOV  oppPF  oppPTS  \n",
       "0      3.5    12.0   20.0    91.8  \n",
       "1      4.0    10.5   24.3    98.0  \n",
       "2      3.0    16.0   19.5    99.3  \n",
       "3      4.0    15.6   18.8    92.0  \n",
       "4      8.8    14.5   20.0   121.8  \n",
       "5      5.4    15.2   21.8    97.8  \n",
       "6      5.4    15.2   24.2   105.2  \n",
       "7      4.2    12.2   23.0   106.4  \n",
       "8      7.0    15.2   21.4   107.0  \n",
       "9      5.8    14.4   23.4   111.0  \n",
       "10     2.8    14.2   22.8   111.4  \n",
       "11     3.0    15.0   24.8   115.6  \n",
       "12     6.0    16.2   24.2   122.4  \n",
       "13     7.2    15.2   26.2   123.6  \n",
       "14     3.8     8.8   19.3   106.2  \n",
       "15     2.8     9.7   20.2   107.7  \n",
       "\n",
       "[16 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "team_opp_stats_df = pd.read_csv('data/nba2.txt',sep=',')\n",
    "team_opp_stats_df.columns = ['Rk','Team','G'] + ['opp' + x for x in team_opp_stats_df.columns[3:]]\n",
    "team_opp_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mergedf = pd.merge(team_stats_df,team_opp_stats_df,on=['Team'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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gfODjVXUe8BPGM/3wFN1893oGv4yeC5ya5M/Gm+rwavAxu4n6qF2S9zKY0rxx3FkAkpwC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a600160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mergedf['PTS'] = mergedf['PTS'].astype(np.float32)\n",
    "# mergedf['oppPTS'] = mergedf['oppPTS'].astype(np.float32)\n",
    "pts = np.array(mergedf[['PTS','oppPTS']].values.tolist())\n",
    "print(pts.dtype)\n",
    "index = np.arange(pts.shape[0])\n",
    "plt.bar(index,pts[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a697e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "team_name = ['DEN','SA','GSW','PHI','LAC','BKN','POR','HOU','TOR','OKC','UTA','MIL','ORL','BOS','DET','IND']\n",
    "plt.ylabel('Score') # 设置y轴的label\n",
    "plt.xlabel('Team') # 设置x轴的label\n",
    "plt.xticks(index,team_name) # 设置x轴的刻度\n",
    "plt.yticks(np.arange(80,141,10)) #y轴的刻度\n",
    "plt.ylim(80,140) # y轴的范围\n",
    "plt.bar(index,pts[:,0]) # 绘制直方图\n",
    "plt.rcParams['figure.figsize'] = (12.0, 6.0) # 修改图片大小\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UmY8DZObjmXlew7VsDPymtSEiDqZ6g9kvM1svHPBPwAkR0a4vWffvy72BpzPz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\nhRiuJUmSpEL+P9kW0Pf9DhZzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a750a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "bar_width = 0.35\n",
    "opacity = 0.4\n",
    "\n",
    "rects1 = ax.bar(index, pts[:,0], bar_width,\n",
    "                alpha=opacity, color='b',\n",
    "                label='OFFENSE')\n",
    "\n",
    "rects2 = ax.bar(index + bar_width, pts[:,1], bar_width,\n",
    "                alpha=opacity, color='r',\n",
    "                label='DEFENSE')\n",
    "\n",
    "ax.set_xlabel('Team')\n",
    "ax.set_ylabel('PTS')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels(team_name)\n",
    "ax.set_yticks(np.arange(80,141,10)) #y轴的刻度\n",
    "ax.set_ylim(80,140) # y轴的范围\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b1sBwFuz/qvreqqrR9gsy93f06xOpcGP6cJLXVNXjq+oZmbsB+G8mXNOGUFVb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\nQjYAAAzs/wfiQ0LQSy8vqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a600b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pts[:,0], pts[:,1])\n",
    "plt.xlabel('Offense PTS') # 设置x轴的label\n",
    "plt.ylabel('Defense PTS') # 设置y轴的label\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t23PRRRfxP//zP3msUJKapoZMF/lvYDjwLkBKaS7gRGL+uUJF+fpKEtUrVPzn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aadd9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pts[:,0], pts[:,1])\n",
    "plt.xlabel('Offense PTS') # 设置x轴的label\n",
    "plt.ylabel('Defense PTS') # 设置y轴的label\n",
    "for i in range(len(pts[:,0])):\n",
    "    plt.annotate(team_name[i], xy = (pts[i,0], pts[i,1]), xytext = (pts[i,0]+0.1, pts[i,1]+0.1)) # 这里xy是需要标记\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107.325 107.325\n"
     ]
    },
    {
     "data": {
      "image/png": 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vc/vnnJO7hvX17QvXX5/bP+MMePHFtY9XVOSuH8CXvgTrLqxx0EFw+eW5/WOP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\nST35AbSGX4HfTBxXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a6b8b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "offense_mean = np.mean(pts[:,0])\n",
    "defense_mean = np.mean(pts[:,1])\n",
    "print(offense_mean,defense_mean)\n",
    "plt.scatter(pts[:,0], pts[:,1])\n",
    "plt.vlines(np.mean(pts[:,0]), np.min(pts[:,1])-5,np.max(pts[:,1])+5,colors = \"r\", linestyles = \"dashed\")\n",
    "plt.hlines(np.mean(pts[:,1]), np.min(pts[:,0])-5,np.max(pts[:,0])+5,colors = \"r\", linestyles = \"dashed\")\n",
    "plt.ylim(np.min(pts[:,1])-5,np.max(pts[:,1])+5) # y轴的范围\n",
    "plt.xlim(np.min(pts[:,0])-5,np.max(pts[:,0])+5) # x轴的范围\n",
    "plt.xlabel('Offense PTS') # 设置x轴的label\n",
    "plt.ylabel('Defense PTS') # 设置y轴的label\n",
    "for i in range(len(pts[:,0])):\n",
    "    plt.annotate(team_name[i], xy = (pts[i,0], pts[i,1]), xytext = (pts[i,0]+0.1, pts[i,1]+0.1)) # 这里xy是需要标记\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Rk_x                   Team  G_x     MP    FG   FGA    FG%    3P   3PA  \\\n",
      "2     3  Golden State Warriors    5  240.0  44.2  89.0  0.497  13.4  32.6   \n",
      "7     8        Houston Rockets    5  240.0  36.6  84.2  0.435  15.4  42.2   \n",
      "\n",
      "     3P%   ...    oppFT%  oppORB  oppDRB  oppTRB  oppAST  oppSTL  oppBLK  \\\n",
      "2  0.411   ...     0.814     8.8    29.6    38.4    26.0     8.2     3.0   \n",
      "7  0.365   ...     0.750    11.8    35.4    47.2    23.0     9.2     5.4   \n",
      "\n",
      "   oppTOV  oppPF  oppPTS  \n",
      "2    15.0   24.8   115.6  \n",
      "7    15.2   21.8    97.8  \n",
      "\n",
      "[2 rows x 49 columns]\n"
     ]
    }
   ],
   "source": [
    "two_team_stats = mergedf[(mergedf['Team']=='Houston Rockets') | (mergedf['Team']=='Golden State Warriors')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     FG%    3P%   TRB   AST  STL  BLK    PTS  oppPTS   TOV\n",
      "2  0.497  0.411  46.0  31.0  7.2  7.2  123.6   115.6  15.2\n",
      "7  0.435  0.365  44.2  18.4  9.2  7.0  107.0    97.8  15.2\n",
      "[[ 0.90363636  0.74727273  0.83636364  0.775       0.6         0.6\n",
      "   0.95076923  0.88923077  0.76      ]\n",
      " [ 0.79090909  0.66363636  0.80363636  0.46        0.76666667  0.58333333\n",
      "   0.82307692  0.75230769  0.76      ]]\n"
     ]
    }
   ],
   "source": [
    "hou_and_gsw_df = two_team_stats[['FG%','3P%','TRB','AST','STL','BLK','PTS','oppPTS','TOV']]\n",
    "print(hou_and_gsw_df)\n",
    "hou_and_gsw_data = np.array(hou_and_gsw_df.values.tolist())\n",
    "\n",
    "hou_and_gsw_data[:,0] = hou_and_gsw_data[:,0] / 0.55\n",
    "hou_and_gsw_data[:,1] = hou_and_gsw_data[:,1] / 0.55\n",
    "hou_and_gsw_data[:,2] = hou_and_gsw_data[:,2] / 55\n",
    "hou_and_gsw_data[:,3] = hou_and_gsw_data[:,3] / 40\n",
    "hou_and_gsw_data[:,4] = hou_and_gsw_data[:,4] / 12\n",
    "hou_and_gsw_data[:,5] = hou_and_gsw_data[:,5] / 12\n",
    "hou_and_gsw_data[:,6] = hou_and_gsw_data[:,6] / 130\n",
    "hou_and_gsw_data[:,7] = hou_and_gsw_data[:,7] / 130\n",
    "hou_and_gsw_data[:,8] = hou_and_gsw_data[:,8] / 20\n",
    "print(hou_and_gsw_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/meituan_sxw/Anaconda/anaconda/envs/python3_5/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['Microsoft Yahei'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n",
      "/Users/meituan_sxw/Anaconda/anaconda/envs/python3_5/lib/python3.5/site-packages/matplotlib/font_manager.py:1316: UserWarning: findfont: Font family ['SimHei'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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KcS5g0KBB2LVrV1NvemoYzREEQegX8zHB6xTaPDBEcIHuZxCRLDk5+Zm8vLy9\nK1asyHj11VeVSqUy3GZxWoHX4+a0hkQiwRNPPCH//PPPDfn5+ZvT0tLeb6jD3cMcqa6uju7rIi0I\nAlVXV0cDONJTY/Is7n4EEQ0xGo0brr/++tSXXnqJh7N7AcePH4dWq4XRaAy3KZwIRhAEPPvss75l\ny5bVl5eX3xAIBHa0/a7QsG/fvkSZTPYWxOpifdnpEwAc8fv994wZM6aqJwbkAt0PICKKj49/ODY2\n9um3335bPXVqOKNhnI5QXFyMQCCAAQMGhNsUTi9g8+bNePTRR31VVVVvlZWVPcQYi4iKWJzO0Zfv\ndjgAiCgmLS1t+/Tp05/Zv38/F+deBi9Wwmkv9fX1UKlU2LVrl/zmm29eYDQajxJRerjt4nQeLtB9\nGCKaYDQaTy1evHjCypUrlTqdru03cSIKLtCc9hAIBHDw4EHk5+dDoVDg73//u+K1117LycjIOKrR\naG4It32czsFD3H0QIpIkJyc/GRcX97uPP/5YPWzYsHCbxOkkTqcTBQUFmDBhQpvHMsYQCAQgCEKr\na6clEgmkUikkEn5v3pcoKCiAVqu9YCqkrKwMN998s7ukpGSlyWS6hzHmDZOJnE7ABbqPQUTxaWlp\nm2bMmDHsjTfeUPJEsN6H3++H2+2G2+2G0+lEUVER0tLS4PP54PV64fV6W1wbTUSN4ttSFTLGGARB\nQCAQaFHA5XI55HI5FApF47NSqYRarYZKpYJKpQKvyR55VFVV4dSpU5g4cWKLf3dBELB48WLvu+++\nW1FWVjaDMfZDGMzkdAIu0H0IqVQ6NSUl5dPnn39ef/vtt3MXKUIRBAEulwsOh+O8h8vlAiC2HmwQ\nRLVajR9++AEjR448TzjlcnlIS4EyxuD3+xvFv+FGwOPxwO12w+Vywe12w+8Xc46USiW0Wu0FD+6Z\n9yxerxc7duzApZdeirZuxr/88kvcfffdDrPZfI/NZvuwh0zkdAEu0H0AIqKUlJRnExISFq5cuVKV\nl5cXbpM4EEXP5XLBarU2Pux2OwBAo9FcIG5qtbpF0f36668jqmEGYwwejwcOhwN2ux0OhwNOpxMO\nhwOMMWg0Guj1+saHVqvldcW7AcYY9u7dC6PRiNTU1Ha9p6qqCvPmzfOcOnXqU5PJdDsPeUc2vN1k\nL4eINGlpaeunTp06afny5QpedCR8uN1u1NXVNT68Xi/UanWjUCUnJ0On03XYy5RKpQgEApBKpd1k\neccgokYP32AwnLePMQaHw9F4Q2IymeB0OiGTyRATE4PY2FjExsa2ejPCaT8mkwkymazd4gwAiYmJ\n+PLLL5W///3vb/jPf/5zmIi8RkpLAAAgAElEQVSmMsaqu9FMThfgHnQvhojSU1NTdzz00EMpjz76\nKL/Z6kEYY7DZbKipqYHZbIbdbodSqWwUoJiYmDZDju1l165dGDFiBDSacBSJCg0+n6/xxsViscDp\ndEKtVsNgMCA+Ph7R0dE8PN4BnE4ndu/ejcmTJ3c6L2DFihXCI488Ul9aWjqdMXY4xCZyQgAX6F6K\nVCqdlJqauvGNN97QXXPNNdwV6WYaBLm6uhpmsxkOhwN6vR4GgwEGgwE6na7bPMKDBw8iMzMTsbGx\n3XL+cNAQ/jebzaipqUF9fT2USiXi4+NhMBgQGxvLPexWYIzh22+/xeDBgy+IYHSUAwcO4Oabb3bW\n1NT8rL6+/uMQmcgJEVyg2wkRBQAUNNl0A2PsDBGNB7AUgBGADUA5gN8zxgqI6FcA7gdwNni8l4im\nAJjLGFvYWVsSEhJ+YTAYXvz000/VfL65+/D5fKiurkZlZSUsFguioqIQHx+P+Pj4Hp1XLSwsRGxs\nLJKTk3tkvHDhcrlQU1ODmpqaxt93UlISEhMTwaduznHixAn4/X4MGTIkJOerrq7GnDlz3CUlJf8s\nKyv7LeOiEDFwgW4nRGRnjOmabUsCsAvArYyxb4PbpgCIZ4ytIaKdACYBeBzAIQDrAGwE8FPGWG0n\nbJClpaX9Ky8vb96aNWuUUVFRXbwqTnMcDgfKyspQVVWFQCCAhIQEJCUlISYmJiwh2D2fLkPa/qVI\nYjWoogSUjH4E466/v8ft6GkYY7BaraisrERVVRUEQUBiYiJSUlKg1+v7rXdtsVhw+PBhTJkyJaT/\nj36/HwsWLPB8+eWXe0wm09WMMUfITs7pNFyg20krAv1nAAJj7I+tvGcXgGkA/gjgGwAJAAyMsb91\nYvxYo9H49dy5cwe//PLLCj5fFzpsNhvKyspQUVEBhUKB1NRUJCUlhWwOubPs+XQZhu9bDDWdS7R1\nMQWOjHmmX4h0U3w+H6qqqlBWVgaHw4GEhASkpqYiJiam34h1IBDA9u3bMXr0aHTXzflf//pX/0sv\nvVRRVlY2mTF2tlsG4bQbLtDtpFmI+zRj7EYiWg3gXcbYJ6285w4AiwB8D+ABAJ8AmMUYu7DKxMXH\nzk5NTd359NNPGxYsWBAZqby9HIfDgZKSElRUVECtViM1NRXJyclhL8ThCwg4XmlDgakeM9ZPQxLV\nXXBMFYvGNZJlUKuU0Cql0Cnl0Cpl0Cml0Chk0Cll0Cql0Cpl0Cpkjfu0Stl5+3XB1wpZ77rZCwQC\njWJttVqRkJCA9PR0REdHh9u0bqW1amGhZuPGjbjvvvusJSUlMxlje7t1MM5F4QLdTlrxoM8T6KDH\nrAewmTH262bHPgngMMSWZXcCKAHwMGPsos2/iWhkWlraN++8845+5syZobugfojX60VpaSlKS0sh\nlUqRlpaGlJQUyGThSYAXBIbTZgcOmyw4VFKPwyYLjpXVYoqwD7dKt2Ca5DBacw7tEh0KVKOwTzEK\nuyQjURGIgcvH4PYyuHwMLq8AoZ0fbbmUWhD4C8Vd3CYNbpNB03Rf8FitUgqlrOfuIQVBQGVlZeNy\nrtTUVKSlpUGtVveYDT1BVVUVfvjhB0yYMKFHIgZHjx7FnDlznKWlpTe53e5N3T4gp0W4QLeT9oa4\niehmALMZY/ObbEsF8AZjbDYRbQVwOYDFAL5ljH3e2pgKhWJ6amrqujVr1mhHjRoV4ivqHzDGUFlZ\nibNnz8LlcsFoNCItLa3Hw9eMMZRaXDhsqschkwWHS+pRUGqB3RMAAGTLanCf7mtc6/8K0f46uNTx\n8LuciILzgnPVIQqBvEmILtkNuUv0sJ2GHNSnj4c1fTzsSUPBSApvAHB7Bbi8DG5fg3CL4t3wukHQ\nxWfxWJdPPL7pPmcHBF8mpUYh1wYFX6eSQdNE3JuKfaPgK6SN+xpea5UyKGUtly5tjtfrRVlZGUwm\nEyQSCTIyMpCSkhIx68c7S0eqhYUSk8mEWbNmuSsqKh4wm83v9NjAnEa4QLeTVgQ6GWKS2E+bJInd\nCeDyZgL9NoBXGWP7iWg3gIkAngBwuLXweFRU1Dyj0fjOZ599ps7Ozu6ei+rDuN1uFBcXo6ysDAaD\nAZmZmT0aAq22eUTP2CR6xodNFtQ6xJkNmQTIipcjN5FwlfwALq3djKTKvQAR6tPHo2bIbNSnT0Dl\n9o2YUfiPC+agvxryKyRNuw5gAtTmHxBdsht6027oKo6AmAC/QgubcQzq08fBmjYePl1Cl6+HMQZf\nAEEBF84JeKOgnxP3hhsCt1c4/3WTn11eAf6Lxo7OIZMQtEHvviGk31zQm3v3UuaHvd4Mt82C1AQD\nBmamITFOD10HBD8S6Ey1sFBisVgwa9Ys99mzZ58tLy9/pscN6OdwgW4nLQl0cPtEAH+BuMyqCkAN\ngKcb5m6IKB/ALxljC4KvfwPgXogh7h8xxi7oJZiUlPSw0Wh8ZvPmzar4+Phuu6a+BmMM1dXVOHPm\nDDweDzIyMmA0Grs9hF3v8qGgwTM2WXDIZEFFvfhnlRCQFidDTpIcAxPlyEmSI1dpRvLJzxBftAEK\npxlejQE1g69DzeBr4NMlnXfuym/WI7/oXSQxMyrJgAOD7xLFuQUkXjv0pfuhL9mN6JI9UDjEAlGu\n2Oygdz0O9uQRYNLIaHjh87cu7o1evvecsDd4+M3FvsH79wXaN66UAE0zQW/qyWuV0iYh+xZC/032\n65QyqOShFfw1B0rxwqZjKLO4kKiT46fDtfjNDZNDdv6O4na7ccMNN3iOHDnybmlp6c/5Mqyegwt0\nBEFElJqa+lJeXt4D69evV/bmylE9SSAQgMlkwpkzZ6DX65GdnY2YmJhuGcvp9eP7MisOlVgaw9XF\n5nNh6JQYGQYmyhrFODtBBrVCAggBRJ/diYTCddCX7AYAWNPHo3rIbNRnTAQkFw/DVn5ficShie0X\nAsagqjsjetclu6GrKIBE8CMgU8FmHC161+kT4I3qO2urfYEmYfkG0W8S3nc4/Ki3eOBw+BGQy+CX\nyc6J/QUePoMv0L7vRgmhhfB9S/P3TZLzGvY1uRHQKqX4uqgKf/z0e7h858ILKrkEz990CW7IN3bX\nr65NAoEA5s+f7/n666+3mEymHzHG/GEzph/BBTpCICKp0Wj8eOLEidd9+OGHynAlLvUmPB4Pzpw5\ng7KyMqSmpiIrKyukBS28fgFFFVYxTF1iweFSC05U2hvnYuN1UgxMkiEnUY6BQQ9Zpzo/I1pur0J8\n0QbEH9sAhaMGXo0B5kHXoGbwtR0Sx+pj1YgbEAepvHPzqRKfC1FlB4Le9W4obRUAAHd0euPctS1l\nJJhM0anz9yaEgABHtQPOGieUeiV0STrIlBd+3vyBpsItNBH/c3P5Fwr7hZ5/ww2A19/571pjjBo7\nfn95Vy67yzDG8Lvf/c774YcfFphMpmmMsQsTJDghhQt0BEBECqPRuOVHP/rR+H/84x98jXMbOJ1O\nnDhxAhaLBVlZWUhLS+tyIlBAYDhZZW8MUx821aOw3NroRenVEgxMPD9UHattZUwhgOiS3YgvXIvo\nkt0AY7Cmjwt6y5e26S23RO0PtYhKjoJcE4LwNGNQ1pcgumQP9CW7EVV+EJKAD4JUCVvqyEbB9kSn\ndX2sCIYxBnedG7YqG6RyKfQp+tD8flvBHzg/PN80ZN/w/ObX1hbfSwBOP9/y1EZP8+KLL/pffvnl\n4rKysjGMsfpw29OX4QIdZohImZqa+s0dd9yRv2TJEnlvSV4JBw6HA8ePH4fdbkdOTg6Sk5M7NffH\nGEOx2RkUYzGJ60iZFS6vOImpVlCjCIvPMiRESdscS26vRvyxDeLcsqMaPnUcagZfg5rB13U5lGw5\na4EqRgWVPvRZvOR3I6rsEPSmPYgu2Q1VvQkA4Nanwpo2TvSuU0dBkPetpUtN8dg8sJZZQRKCPlUP\nhTY8kYT7l1ehxnZh9lxKtArfPRY5yyzffPPNwFNPPVVSVlY2mjF24WJ9TkjgAh1GiEiVmpq6Y8GC\nBSOefvrpyMjciUBsNhuOHz8Ol8uF3NxcJCa2fy6WMYYKq7txnXFBqfhc7xKn0BQyQnaCOGfcIMqp\nsVJI2iv8QgB60x4kFK5D9NmdorecNhY1Q66DJXMSIAnNVIWt3AapQgqNofvzEhTWUkSX7IW+ZBei\nyg5C6ndDkMhhT7mkMdnMHZOJVhdp92I8dg9sZTYAQFRqFJS6nq0BvrXQide3WOFtptHDU/X49JdT\nIJFEzu98+fLlgcWLF5eVlZWN6kzpYk7bcIEOE0SkMRqN395///1D//CHP3BxbgGn04ljx47B6XQi\nLy8P8fHxbQpzrcN73jrjQyUWVNvFZUpSCZBpkGNAMFSdkyRHepwMMmnHv/TkjhoYjn2G+KL1UNqr\n4FPHomZQ0FvWp3Tqei+Go8YBwS8gKrln669TwAtdeQH0JnHuWl1XDADw6BJhTRuP+ozxsKXmQ1Bo\ne9Su7sbr8MJaJoabo43R3Rr6boql2ILdlQz//d4Hs02AIUqCYalybD3mwf3TBuCxa0PTICNUvP/+\n+8Lvf//78qBI14Tbnr4GF+gwQEQqAK4f//jH7MMPP4ycW+IIwePx4MSJEzCbzRg8eHCrHrPN7UNB\naT0KTPXBjOo6mOrcAMQ5O2OcrHHeOCdRjswEOZSyLvy6hQD0pr2IL1qHmOLvQEyA1TgG1UPmwJIV\nOm+5JVwWF7x2L6LTwlvOUm6vbJy71pfuh9TnBCMp7MnDG+euXXED+ox37bF7YC21inPURn2LyWSh\nwl3vhr3SDkOu4bz/d8YY3vraho0FTjx/0wj8ZHxGt9nQGT788EPh4YcfrigrKxvJRTq0cIHuYYhI\naTQav7vvvvuGjxs3Tp6YmIgxY8aE26yIwO/349SpUygvL0dOTg6MRmPjF5XbF8D3ZVYUmM4tb/qh\nxoGGf98kvRQDgiHqnCQZBiTIoVGGJtlO5jQjvqjBW66ETx0Lc94sVA+5Dl59zyx98Tq8sFfZEZcd\n1yPjtQvBD13F943etcZ8CgDg1RhgTRsnetfGMQgoe3fXNcYYPFZRqBVaBaJSozqdTd8agl9AdVE1\n4vPiIVVceO6AwPDcp3U4YvLhvQXjMWlgZNVHWLFihfDb3/62LCjSPNwdIrhA9yDBbO3t991336gn\nn3xS7vV6sWXLFvR3kWaMwWQy4eTJk8jMzIQxPQMnqhyN88WHSiw4XmmHP7i+KVYrPc8zHpgkh14d\n4sx3JkBv2of4wrWIKf426C2PFjOxMyf3eLEPv9cPS7EF8bmR9cXcFLmjBnrTHuhL9kBv2guZ1w5G\nEjgShzauu3bG5wDUO1cpMMbgqnXBVm6DJl4DXaIOFII5YcYYak/VQmPQQB3beiKewyPgiZW1sLqA\n//1iMgYkXFA3KawEw92mYLibJ46FAC7QPQQRyY1G4zc/+9nPxvz5z39u/Hbv7yJtrq3F5zsPo9Kn\nQo2gwZEyG46WW+EJ1oHUKcXlTQOT5I3zxnHa7ivVKHPWIr5hbtlWAZ8qBuZBV6Nm8LVhXXbEBIbq\nomokDk0Mmw0dQghAW1XYmBmurT4GAPCpYmBNG4v6jAmwpo1FQNX7OlAxgcFWYYOrzgW9UQ91TNey\n2x01DnhsnnZFRyrr/Xjs41rEaFRY84vJiA1TtnlrLF++XFi8ePHZoEjzJVhdhAt0D0BEEqPRuPnW\nW2+dtnTp0gtcr/4i0owxmOrEhhH7z9Rg14kKnKzzwh2sSaSSEwYkyMXiH0HvOCm67eVNXTdMQFTp\nfiQUrkPMmR0gFoA1dRRqhsyBJWsymDQyvgQrv69E0rCktg+MQGSuOuhNe4Pe9R7I3fVgIDgTBjXO\nXTsSBnVqjXi48Hv9sJZYIQQERGdEQ67qeFTF7/HDfNKMhMEJkEjbF1koKvfiqdW1GJ0Rh/cXTIi4\ndqHLli0LPP3006eCIu0Ktz29GS7Q3QwRkdFofG/WrFnz3nrrLUVrYtMXRbrK6m5cZ9zQNKLOGWwY\nQUBGnBS5qYpGz9gYK4O0B5eRyJy1MBzfiITC9VDayuFTRcOcNws1g6+DJya9x+xoL71ZoM9DCEBT\nc6KxyYe2qkhs8qHUw5o2RhTstHHwayJovv0ieGwe1JfUQxmthD5F3+6wN2MMNcdqoDfqoYzq2HKu\nb4pc+PvmetwyJg1Lb74k4pp/PP/88/7/+7//21daWjqFlwXtPFygu5nU1NRnx44d+/CaNWuUbVUI\n680ibXF6g3PG9ThUIjaMqLSeaxiRbpAhO06CFIkPOclyDBkcDWULyTDdDhMQVXoACYVrG71lW8oo\nVA+5DpbsqRHjLbdEVWEV4vPi2+1p9Rakbiv0pfsam3zIXWKOUfMWmt2ZJd9VGGOwV9rhNDsRnR7d\nroIy1nIrWIB1OjP/w502rNztwKNXD8YD0wd26hzdycKFC72rVq36zGQy3cgbbHQOLtDdSEJCwgN5\neXkvf/3110q5vH3hr94g0g6P2DCiwTM+VFKHs7XnIlmpMbLzalRnxknhrbbBa/ciJjMGCk3Pi6DM\nVQfDsU2IL1oHlbUMfqW+MRPbExNZy1Zaw3zSjOi0aMhUkStUXaaVFpoBuTboXYeuhWZ34Pf4YTlr\ngUQmQXRadKvZ3l6HF5azFiQMSuh0ohljDC9vqseO4268fvtoXD089OvvuwJjDLfeeqtn27Ztb5lM\npl+G257eCBfobkKr1V6fk5Pz0fbt21VRUR1bZhJJIu3xB1BUbmsixhacqm7SMCKqSUZ1khwDEs5v\nGOGxeWA5a4E2QQttgrZnQ3FMQFTZQcQXrkPMme2QCH7YUi4Jrlue2usaQ9QV10Fj0PR4datwcq6F\npphs1nILzeERFfloqPFtLbMiKjUKmrjzq78xgaGqsApxA+IgV3dtNYDXz/DH1bU4aw5g5f2TMCLM\n6+SbEwgEMGvWLM+RI0f+WFFR8Zdw29Pb4ALdDUil0ksHDBiwZdu2berk5M7VYA6HSPsDAk5W23G4\nRFxnfMhkQVGFDf5gw4hotUTMpm5oGpEkR4ymZQ+BCQz1pnr4XD7EZsV2a4GH5shcFhiOb0R84Xqo\nrKVBb/kq1Ay+Du7YzB6zI9RYS62Qa+QXXYrTp2naQtO0B7rywxHdQlPwC7AUW8DAEJMZA6lM/KxY\nii2QqWXQJYZmmZTFGcBjH9cCTIpPfjkFKdGR9f/hcrkwdepU98mTJ++xWCwfhNue3gQX6BBDREMz\nMjJ2ffHFF7rc3Nwunas7RZoxhjNmZ3CdsZjA9X1ZfWMfWk2wYUTD8qaBiXIkRLVveZPH7oGluIe9\nZsagKz8oZmKf3g6J4IMteQRqhsxGXfZlvc5bbgl7lR0gQBdh61/DRW9poemqc8FaaoU+TQ8iarFa\nWFc5a/bh8ZW1yDbosPLnk6DtwRvi9mA2mzFlyhTXqVOn5ni93i3htqe3wAU6hBBRutFoPLx69eqY\n8ePHh+ScoRBpxhjK693nZVMXmOphDa5vUsoIWQnnF/5IielAw4iGcQQGa5kVXrtX9Jp7YK5U6q6H\n4fgmJBSug6reBL9Cd85bjsvu9vF7EletCz63D/pUfbhNiTwivIVmwBdA3Zk6eGweJA5N7NSSrLbY\nf8aDJWvrcPngJCy7Y0yProhoD8XFxZgxY4bz9OnTUxhjB8JtT2+AC3SIICJtamrqsTfffDP12muv\nDekno6lI/69EgRW7ShBgDFIi/HRCOp65YcR5x5vtnsZymA3LnGqaNoyIF8PUDd2b0g1dX97kd/tR\ne7oWqhgVopKjutdrZgy68sNiJvbpbZAIPtiThqN6yGzUDbgMTNY352g9Ng9ctS7EZMaE25SIh/xu\nRJUfbvSuw91CkzEG80kzZAoZPHYPYrNiu6Wl5YZDDry91Yb7pg3A4xHWWAMADh06hNmzZ9ebTKah\njLGycNsT6XCBDgHBQiTfLFy4cMLDDz/cLW6j1+vFPa99jm/KL9x3xZBEjMmMw2GTBYdNFpRazjWM\nSIsTM6obxDgrXg5FVxpGtIDT7IStwobYzFgodN0XThS95c1Bb7kEfoUWtblXoXrI7D7nLbeEz+2D\ntdQKw0BDuE3pdSisZee867IDPd5Cs2m1sIabWXWsGrokXchvZt/82oqNhyOzsQYArFmzhv3yl78s\nLi0tHcIYc4fbnkiGC3QIMBqNL8+cOfOB9957r1tdt4GPrUfgIn+u5GjpuXnjRLGtolrRfWtmhYAA\ny1kLIAAxWTHdsz6XMegqChBfuA6xp7dCEvDBnjQU1UPmBL3ltteb9hUEv9BYdYrTeSjgha7iCPQl\nuxBdsgfqujMAzrXQtKaPg9U4OmQtNFuqFsYEhvrSevhdfsRmx4a0+UZAYFiy1oKCEi/evXs8JudE\nXv32Z555xrds2bKvTSbTLL5GunW4QHcRvV7/k6FDhy7fvn27Sibr3jnXrN+vb3XfO/cmIirUDSMu\ngs/lQ+0PtdAl6aCND30vYKnbCsOJzYgvXA+1pRgBuRbm3CtQPXQO3HEDQj5eb4AxhqqjVX2jmlgE\n0Z0tNBurhaXpW1we5653o76kHjGZMR2uJnYxHB4Bi1fVwuIA/vfgZAyMwMTCW265xbN9+/al5eXl\nT4bblkiFC3QXIKKRAwcO/Hbnzp2a+Pjuv0sd+NgGBFr4e0kIWPmrnlta4qx1wlZuQ1x2XGgb2TMG\nbeURJBxde85bThwiZmIPmN5j84WRTJ8p9xmphLiFZnuqhfm9ftSeCn3Iu6GxRrRaiU8enBJxjTU8\nHg8mTZrkLioqmudwONaG255IhAt0JyGiRKPRWLR+/frYkSNH9siYi9cU4N87z16wfdYIFe6b0f2J\nQ4wx1JfUw+/xIy47DpIQFemXemyIO/E5EgrXQl13zluuGTIbLkPklTAMJ5XfVyJxaGLE1V7uq4gt\nNPeK3nWLLTTHwxmf29hCM/bEFzDueQsKezU8mgQUpc+Df+qNbVYLYwKDpcQCwS8gNis2ZNNFx8q9\n+OPqOuRnxOL9BeOhlEVWM5KKigpceumljjNnzoxljBWF255Igwt0JyAiRWpq6pGXX3554Lx583q0\nMPLE575ARZMa15OSBNw5RtntiUMBXwC1p2qh1CsRlRKCLG3GoK38HgmF6xD7w9eQBLxwJAwS55YH\nzuDecivUHK8J+Zwlp5200ULTr9Ai4dhGSAKexrcEpEoUT3sYdblXtGsIR40D9ko74gbGhWwp1rZj\nLvxtUz3mjk7Di7dEXmONXbt2Ye7cuTWlpaV5vI/0+XCB7iDB7lSr77zzzuuee+650C9mbINxz3yO\nYekS/OpK0WMW/AIqCiqg1HefSPucPtSerkV0WjRU0V1LypJ67Ig7sRkJheuhrjuNgFyD2pyZqB4y\nG674rhV26Q/Unhbn/cNRz5xzPi210GwJjy4RR279sN3n9Tq9qDtd1+6mG+3h4112fLTLjt9dPQi/\nmJ4TknOGkuXLlwf+8Ic/HC0tLR3Nu1+dI7LKzfQCDAbDQyNHjrz2mWee6XFxrrF7UG33Iiv+3PyX\nRCZB8ohkVBRUwHzKHHKRdlnEKkhdqhvMGLRVRxFfuA5xp76GJOCBI2EQiqcuQm3OTO4tdwCJTAIh\nWO2NE1786ljU5l6J2twrASZg9JtXoCXfVGGv7tB5FRoF4vPiYT5pht/tD0lJ0FvGa1Fa58fSjceQ\nbdDimhGR1VjjZz/7mXT//v2D/ve//70O4J5w2xMpcIHuAER0SW5u7pIVK1Yo2mod2R0UllsBAFnx\n5//ZukOkG9rnuevdiB8U31hHuCNIvHYYTnyB+MJ10NT+gIBcDXPulageOhuu+Lwu29gfkcqlCPgC\n4TaD0xySwKtLhNJedcEubyc6b0nlUiQMSkDt6Vr43X5Ep0d3KTRNRHjwimhU2wJY+PFBGGPVuCQt\nsgrevPzyy4o9e/bcrtVqP+FJYyJ9q7FsNxKsFPb5Bx98oNbrw1Nq8ZxAX+jJNoi0x+qB+ZS5S+Mw\ngcFSbIHf5Ud8bgfFmTFoqgqR+fVSjHz/FmTseAVMIkXx1EU4fNtKnJ22iItzF5DIJBD83IOORErH\nLUBAcuHUgz1pRAtHtw1JCHEDxGRM8wkzhEDX/u4KGeHR62KgVxMWvLMHZRZX22/qQWQyGVauXKk0\nGAwfEJEx3PZEAtyDbifp6emrfv3rX8eNGzcubDYUlttg0ElbXe8cCk9aCAhiMliUErrk9i/5EL3l\nLYgvWgeN+RQCMlVjJrYzYVCH7eC0DEkIfpcfHrsHgl8ACzAIfgGCIIAJrPGBllJLSHw/SQhEBJIS\nJFIJJDJJ47NULgVJKeISiXoDpthJcAx1IO/Mh1DYq+HVJcCnioXh1BbUZ05EXc7MDp+TiKBP1cNp\ndqLmWA0MuYYuJQhGa6R4bE4MHl9ZiwXv7sGqCGuskZ6ejldffVX7wAMPfElEw/r7fDRPEmsHcXFx\n906cOPGV9evXq8L5xTXrb1uhVfnwxPWxFz2us4ljAV8A5pNmsQtVe4qPMAZN9TExE/vUl5D63XAa\nclA9ZLY4txyiSkz9CcEvwO/2w+8JPtx+BLyB87wnwS9AFa0ShTUoriRtIrzB5/MmRJk4bcEYO0/I\nBb8AISCIz34Bgk9oHIukBJlcBqlKCplSJj5UMkhk7etq1p9oqVoYAJDfi9zPfgdt5VGcvPo52NLG\ndnoMt1UsamIYaOhyI5oDZzx4bm0dZgxOxBt3jI24xhq/+tWvPKtXr36jtLT0oXDbEk64QLcBEQ0e\nMGDAvn379mliYsI3Z+PxBzDsyU24frQGt01qu0hCR0W64QumPZnaEq8DcSe3IKFwHTTmkwjIVKgb\neDmqG7xl/uXdJkxg8Ll84sMpPgs+ASQjyJXyc6KokkGmkDV6tQGv2BUpPq/7C+MIAQEBb6DxRqHh\nudFOtVx8aORQaBRtrvXtq7RVLUzqsSNv7W+gtJXj+OyX4Uzo/BSP1+lF3Q91iM3uerONzw458NZW\nG+6Zko3Fs4d26VyhxnNtU+cAACAASURBVO/3Y8KECe6CgoLZ/bk9JRfoi0BE6tTU1JMfffRR6pQp\nU8Jqy/dl9bjule1YdHU0Jue1L+u5vSLd3g+9pvoY4gvXIu5kg7c8sIm3HHmlBCMJv8cPr90Lr0N8\nMIE1iptcIwpde0KXjDFUFVYhaWh4q4kJfuG8GwyvU+yWptAooNCJD5lS1i88bWu5FUxgiDa2Xi1M\n7qjBoE9+CUnAh6IfvQKvvvNTrB25mW6Lt7daseGQE8/dOAK3ToisxhqnT5/GtGnTrCaTKY8xVhlu\ne8JB5Ew+RCDp6env33PPPQnhFmcAOFomJohltpAg1hrtmZP22r2oK65DXE7LhREkXifiTn2J+MK1\n0NacgCBVonbgDFQPnQNnwmDuLbdCwBuA2+qGx+aBz+GDRCGBUqeEKkYFfaq+01XYiKjl+eUeRiKT\nQBmlPK9+tBAQRLF2eFFfUo+AJwCZWgZllBIqvapH+oP3NF6HF26LGwmDLp6p7dPG48S1SzH4k4eQ\nu+FRHLv+Ffg1cZ0aU6aUicuwTpjBBAZ1bOeXKc6fGoUKSwBPfnIEmQZNRDXWyM7OxgsvvKB75JFH\nPieiUYyxfpcdyT3oVoiJibk1Pz//X1u2bFGGY0lVc55eexQf7DqD93+e2OH5otY8aY/NA8tZCww5\nBsiaJYqoa44j4eg6xJ3aAqnPBWfcANQMmY3anCsQUHJvuTlMYI1f1m6bGxKpBEq9KGChDv/2lnKf\njIlhfI/NA4/Vg4AnIIp6tCjYvT0kzgQxmtGRGgHayqPIW/cwXLEZOD77ZQgKTafHF/wCak7UQJek\ngyau8+dxegQ8saoWdQ7gf7+YjJwQrLsOJXfffbdn48aNL5WVlT0eblt6Gi7QLUBEyRkZGSd2796t\nS0qKjMYEP31jJ6odVvzlx51b49xcpBsSTuJz4yFViKFVic+FuJNbEF+0HtrqYxCkCtQOnIGaIbPh\nSBzKveVmMIHBXe+Gq9YFn8sHhU4BVbQKSr2ye1pvBqkuqoYhxxCyWug9BRMYPDYP3PViZEGqkEId\nq4Y6Rt3rrgUALMUWyNSyDhcS0Z/diZxNi2FLzcfJq58Dk3a+5pEQEGA+YYYmXtOlrnJV1gAe+9iM\nKJXYWCMughpruN1ujBkzxnX06NFLGWOHwm1PT8IFuhlEROnp6buWLl065ic/+UlEfGswxjDq6c0Y\nN0COB2a2Ps/VFg0iPaLkXWRUbAFBAEiC2qypEFR6xJ3cAqnPCVdslji3nHtlu7v29BeYwOCyuOCq\ndcHv8UOlV0Edq4ZcK+8xj9Z80gx9mj5ktZrDhc/lg6vOBbfFDYlMAnWcGupYdbfe3IQKd70b9ko7\nDLmGTv3dDcc+Q9bWF1A78HKcvvzxxmYbnUEIiH3CNXEaaBM6L9L/z957h8dR3mv/9zNld7b3VdtV\nsSR3jME2GNuA6djHQDDBkARIQm8BTgglhPACJ6GG8JJfyMUhLwkh5ZDKIYUaEkgICSV0LFtyka1m\nbe995/n9sZaQbZUts7szsj/X5T8szc48Wq3mO8+33HfvSAZ3/C6IpW4zfnbp0bIy1njrrbewcePG\n4aGhoXZKabbe66kVs68oVCEmk+lLRxxxxBK5BGcA2BNJIZzMoc1emSQmwzE40v8LNO95+dMJHCrC\ntvM1iCAIdp8C74INiDcsOrRbngClFJlYBgl/AplYBmqTGoZmA3hN7YLyRFieLch9SiPTXDfGusCN\nzUbkUjkkAgl4t3jBCRx0dh3URrUs0/j5XL6QfZpnL3t9/nnrwCeCaHn7/yGrtWJw5VVl/80xLAN7\nd0EaFEDZQXpukwrXnmLEwy8E8fXffYSHzj1cNu//UUcdhU2bNtl/+ctfPgDgP+u9nlpxKEBPgBDS\n1Nra+r0nnnhCOud0CZhOQawU0pE0mna8MKleMCEE/SfcWtH5Zxv5bB4JXwKJQAK8hofWroW5zVz3\nmxbDzz49bk7gYGw2wtBkQDaeRcKfQHggDMEsQOfQHdAjUS8opQj1h2BsMVbsKLZn6efAJ/xo+Og3\nyGptGD38vLLPRRgCW5cNvj4fCEOgtZVXk14zV4PhYB6/fHMInQ49rjlBPsYa9913n+rFF1+8ghDy\nE0rp+/VeTy2Qx6deBhBCiMvl+sMDDzygtdvl08kIFBTEAKDNXv6vKx1LIzQQKqS1J+Pga5CclLHd\ncswTQy6dg86uO0B8ot4wHIN8bnbqcRNCxse0qEiRDCYR3BkEYQh0Th0Ek1DXB6SEPwHCkoo6p8ch\nBAOrrgGXDML15n8jq7EgMPfU8k+3N0j7+/wAQdmNY+cepcNwKIcHX9yKdpsO/7FEHsYaKpUKTz75\npObss8/+EyGkg1Kaqfeaqo187jp1xmQyXbJs2bJF5513nuzek80jETiNLHTq8paWiWcQ2lXo1p6y\n1lVBDWw2QEWKhD8Bb48XcW8c+gY9nAuc0Dv1sgrOwIQU9yxnbCfomO+AyW1CKpyCZ7MHMU+sYl3q\ncsilc4iNxmBulVCwiDDoP+FWRJqXov21B2EceKui0zEsA1u3DbHRGJJlam0TQnD1SSbMb+Lx1V+9\njw8GQhWtSUpWrFiB888/397c3PxgvddSC+R156kThJBmk8n08I9+9CNZVvV6hiNl756zySyC/cHx\nUSrP/A0HjNFSAANNp1S8TiUi5kVER6LwbPYgm8zC1mWDdY4Var08659AIcU9W3fQU8FreFjaLHDM\nc4DmKbw93sKsdY2cvSilCOwMwNxmlvyBjbIqbD/1biSt7Zjz8p3QerZUdL6xmnR0OIpUJFXWOVQc\nwc3/YYZJS3DpT97GkIyMNe69916V2Wy+jBByRL3XUm0O+gC9N7X9+wcffFBrtZYnHFBNkpk8+v3x\nAywmiyGXySGwIwDrHOt4DW/w2BuQNLeBohCYKWHgmX8mPmi5sGIXLCUh5kSEh8Lw9nhBGALHAgdM\nLtP4yJmcYbmDYwc9GQzHwNBkgHOhE7yWh6/XV3Bey1TXUyE6Ei3McE8i5SkFokqPvnX3Iacxo+uF\nr0MdGqjofAxX2EmHB8LjKm+lYtKyuO0MC+KZLC558m3E0vLwreB5Hj/5yU80LS0tfyKEyGcerAoc\n9AHaaDRevGzZssWbNm2S5XuxdTQKkZbeICbmCqMX5lbzASIKlNcg2rwU717+F7x72Z8xeNwNkllV\nyh0xJyI8GIZ3ixesioVzoRP6BvmlsaeD4ZmD3hN6LP3tXOiEyqBCYFsAwf4gclUIIpl4BqlwCsam\n6trM5rQ29K27HwDQ/fwt4BKBis7H8ixsnTYEd5T/vrhtHL66zoze0Siu+5/3kBflMZa7fPlynHfe\nefampqZZnepWzl2pChBCLEaj8eHHH39cVl3bE/m0g7v4HTQVaWFWtsm4jxQjAJB8FprAdiTs+1pA\nSuknLUfEvIjIcKQwxqPmCoHZoVekmhVhSMFS8hAghEBr1cKxwAHBJMC/3Y/Q7pBkDzBiXkSwv6BT\nX4vPStrsxrbT7wWXDKH7+VvBZOIVnY8TOFg6LPBv85f9nhzRpsYlxxvxly0e3PNcT0XrkZJ7772X\nNxqNlxFCuuu9lmpxUAfo1tbWx7761a9qnU5nvZcyJT0jEWh4AqepuNQrpRT+7X5obdpJO02FYD+Y\nfHZSj+bZGKSpSBHzxMZT2c6FTugcOkUG5jHkWhuvJ4QUOqudC5xQ6VTw9foQGY5U3EwWHgxD59DV\nVBQm4ZyPHafcCU1gJzpfugMkX1mzskqngsllgn+bv+z34/QlWqw/XIsnXt+Jn7+5q6L1SIVKpcLD\nDz+scbvdvyWz9I/ioA3QhJClFovljOuvv17WRcfNI4UGMabIz194IAxey08pVqDzbgUAxKewvJtN\nQToVTsHT40E+m4djgQOGRoOiA/NECDm0i54MQvamvhc4wbAMvD1eJPwJlKOYmAqnkE/nK1LnKpeI\n+yj0H38TjMPvof2v91U8BimYCvPkgR2Bst4LoGCscWS7Gnc8+zH+3uetaD1SsW7dOixZsqRbo9Gc\nU++1VIODMkATQhiXy/W7xx57TCMHI4ypoJRiy0ik6PpzzBtDPpuHsXnqWpnW24uc2oCMYerZRqUH\n6WwqC1+fD3FfHLYuG0wtJkXVmIvhYOzkLgXCEOgb9HDMdyATy8C7xYtMrPidaD5bUAuzdFjqlrEI\nzD0Vg0dfDuuOV+F+41GgQllmnV0HXsMjMhgp6/UsQ/DV001wWTlc/fN3sc0TrWg9UvHYY48JVqv1\ncUJI+Y4hMmV23bWKxGw2X3b88cc3rVy5st5LmZbBYBKxdB5tjpnrz6lICglfAtYO67Q3FK1vKxL2\nuTPKCioxSFORIjwURnBHEIZGA2ydB7p0zRYYbvapiVUDhmNgbjPD0m4pfDb6gxBz079vlFKEdkmj\nFlYpo0vOw+hhn4Xzk2fQ8MH/VHw+Y4sRuXQOcV95tW2NisGtGyxgGRFffvJtBOL11wpxuVy47LLL\n9LNxNvqgC9CEEIter3/wkUcekeXM80Q2F9kglk1lER4Iw9ZlmzaFS3IZaAI7EZ+k/jwZSgrSqUgh\nnc1wDBwLHAc0x802WJ6dMdAc4lN4DQ/7XDvUBjW8W6ZPeyf8CTAsI41aWKUQgsGVVyLQeSJcb/0/\n2La+UOHpCCwdFsQ9caSj6bLO4TSyuGWDGaORFC7/6TtIyyCTc9ttt/F6vf5Ls61h7KAL0G63+7Eb\nb7xRa7OVZ9tYS3pGIiAAWm1TB2gxJyKwPQBLh2XGp31NYAcYMYfEFPXnyZB7kBZzIgI7AgVnoS4b\nDA2Gg6KJiuEOjVqVylh92jHfgXQ0DX+f/4DxozG1MFNr+a5xkkMY9K+9BZGWZWj723dg3P2vik7H\nsAxsXbbC/HiZ41dzG1W49mQj3ukP4tbfflR2XVsqeJ7HI488onW73b+ZTQ1jB1WA3tsYduZ1110n\n68awMXpGImiycBD4yX9NY+pGhiYDVNqZ5/W1vrEGseJ20GPINUgnQ0l4t3ghmAXYu+2zNp09GQeL\n3Gc1YDgGlnYLDE0G+Lf5EffGQSmtqlpYpVCWx/ZT7kLC1onOl++CbnRzRedjVSzM7WYEtgfKbjZc\nPVeDz63U45n3hvDoX7dVtB4pOP3007FkyZJ5Go1mY73XIhXy+hRWkb2KYb979NFHBTk3hk1k80gE\nbdPsnqMjUXACV7Qovs7bi6xgRlZX+liZnIL02K454UvAPs9etimAkjkkVlI5aoO60ESWyMDf50d4\nMFxVtbBKEVVabDv9XmR0NnS9cBvUod0VnU+tV0Nr1yK4K1j2DvicFTocN0/Ad17qxR8/HK5oPVLw\n6KOPqq1W6w9nS8OYMiKVBAiCcN7RRx/dvGbNmnovpSiiqSwGAkm0T9EglgqnkI6mYXIVn4rTercW\n0tvl+s7KIEino+nCrtkkwNpprXsTT71guUM1aClgWAaWNgsEs4C4J17TeedyyGmt6Ft/PyjDovu5\nm8HHfRWdT+fQARRlN41NNNa48Vcf4L3dwYrWUyltbW244IIL9I2Njd+s60Ik4qAI0IQQ3mazff+h\nhx6S56PxJGzZUxhhmKxBLJfOITwQhnXO9B3bEyG5FDTB/pLT2/tTryBNKUVkKILIUAS2bhu0Nu1B\nUWueikM7aOkQ8yLi3jjs8+0Fe8v+YF3csoolY2wpqI2lo+h6/law6VjZ5yKEwNJuQcKbKGkMbSI8\nR3Dzf1hg1hJc9tQ7dTfW+OY3v8nzPP8VQoj8zBVK5KAI0Far9eoNGzYY2tra6r2UovlU4nM/HW2R\nIrCjUCcrZfeo9W8HoWJhxKpCah2kc5kcfFsLOwX7vIOr1jwVDMcc2kFLxJhamFqnhrXTCpVOBe8W\nL7KJbL2XNiUJx1xsP+UuCKHd6HzpdpBc+eNOhCGwzrEiuGvmEbSpMGkZfP0MCxKZLC5+8q26Gmvo\ndDrccMMNgsvl+k7dFiERsz5AE0K0Go3m7nvuuUdRric9IxHo1Qxs+n1/RZGhCDRmTcljRNq9CmKT\nSXyWQ62CdCqSgr/XD2OLEcYW40G9a57IofdBGvZXCyOEQOfQwTrHisDOABL+RJ1XODVR13L0r70F\nhpEP0fGXbwNi+RkVTuBgbDIi2F9+Pdpt4/C19Wb0jcZw3S/erauxxnXXXccKgnAeIcRVt0VIwKwP\n0E1NTbd/8Ytf1MjRSnI6Ng9H0O7g9rkRp8IpZBIZ6Bv1JZ9P5+1FRmtDVmeXbI3VDNKUUkRHoogO\nR8fnVw9xIPUeb1Ey06mF8RoejvkOJINJhHaFZCurGuw6CQMrr4Kl/+9wv/H9itTGNFYNGI5B3Fu+\nQcfhrWpcutaIv2z14lt/qqzTvBI4jsM3v/lNobW19b/rtggJmNUBmhBi5Xn+uttuu03enR/7kRcp\nto5G0Tah/jx2Myml7jyRcQUxialGkBbzhdnufDYP+1y7Ijya6wHDMaB5eQYOuVOMWhjDMoVGRDUL\nb69XtjV/z5JzsWfJeXBufhaN7/2sonOZW82I++LIJstP7592mBYblmrx43/046f/qp+xxgUXXMCY\nTKYTCCEL6raICpnVAdrlcj143XXXCTpd7cXuK6HfH0cqK443iFFKEdwZhNFVnvQgk01CCO6WLL19\nwPklDNK5dKHerLFoYG41zxpzi2pwSKykfIpVCyOEwNBogLHZCN9WHzKJ+ktbTsbQ0ZfB330KWt75\nMWxb/lT2eQhDYG0vpPcryRpctMaAZe1q3Pnsx/hbb32MNRiGwX333adpbW19si4LkIBZG6AJIS5B\nEM6Xu1vVZOzfIBb3xMEJHDTm8qQHtb4+ENApHaykQIognY4V1J1MrSZobbNijLGqHBIrKY9cqnS1\nMMEowNplRXBnEMlgfbuUJ4Uw6D/+JoRdK9D294dh6v9H2afitTx0dh3CA+Gyz8EyBP95ugluG4er\nf/5v9I3Wx1hj/fr1aGlpOYwQIm/jhSmYtQG6tbX1sdtvv13gOOV1/PaMRMAQwGXlkE1lEffFS5p3\n3p/xBrEqpLgnUkmQTgQSCO0OwdZtk61QhNxguEOOVqVCKUWgvzy1MF7g4ZjnQMwTQ3SPPJyc9oHh\nsOOUO5Gwd2POK/8F3Z6Pyz6VzqFDNp1FKpIq+xwaFYNbz7CAYykufvJt+GPlaX9XykMPPaRxu91P\nKVECdFYGaEJIl8FgOPHCCy9U5M/XMxKFy8qBZ4FgfxCWNktFqV6trxcZnRM5bfUb5coJ0rHRGOLe\nOBzzHIdGqErg0A66dKIj0YrUwhiOgb3bjmwii9DukOya9EReU1Ab0zvR9eI3IAT7yzoPIQSWNgvC\nA+GKZsIdBha3bjBjNFow1kjVoSRzzDHHYOHChW4AJ9f84hWiyAA2E62trd+99dZbFSPpuT+bR8Jo\ns3OIjcag1quh0lc2Iabzbq1qent/ig3SlFKEB8NIx9Kwd9tlp38sdw55QpdGJp5BKpyCsWlqv/Ri\nIAwZ7/wO7KisVlsNchoz+tbdD8rw6H7uFvCx8mrAnJqDvkFfUaobALobVfjKKSb8e1cIX/9dfYw1\n/uu//ktwu92P1vzCFTLr7oiEkGZBEE76/Oc/r7h0BgCEEhnsCafhNjFIBBIwNld2M2EyMQjhwao1\niE153RmCNKW0MHMp0kJn+qFmsJJhuUM76GIR82IhG9VRWTZqDEIITG4TVHoVfH0+2SmPZYxN6Ft3\nH9hMHN3P3wI2XV5KXmvTIp/NIxUuP9UNAKu6BXzumIKxxv/3l9oba6xYsQIdHR0uQsjRNb94Bcy6\nAO12u++5/vrrFbx7LjSI2bLJilPbQKFBDEBNd9BjTBWkqUgR2B4Ap+YKndrKKw3JgkNyn8UzphYm\ntda2ocEAnV0nyyCdtHdh+6l3Qx0eQueLt4PkSq8BS5XqBoBzlutw/HwB3325F3/4oPbGGnfddZem\nra3tBzW/cAUoM4pNASHEyrLsZy+//HLF/lw9I4Un3TmNaqh0lYuf6WrUIDYV+wdpKlL4t/uh0qsq\nzg4c7LD8IcOMYkiFU8hnPlULkxqtTQu9Uw9fr092v49oy5HoP+Hr0O/5GHNe+VZZamOsioW+QY/I\nUKSitRBCcNWJJixo5vG1X9feWGPt2rVwOBzzCSGLanrhClBsIJsIIeRHhBAPwzC9V1xxhXr/zm1K\nKa677jp0dXVhyZIlePfdd+u00pn5eDAIAw+45kgTvLTeXqQNTcgL9TOgHwvSqXAKQ/8egmASYGg0\n1G09swXCENnVP+XGuFpY+4FqYVKitWphaDLA1+uTXVYj2LkWA6uugXnXP9D6+iNlqY1p7VpkE1lk\n4pXNgY8Za1h0BJf+5B0MBmsrpXr77bdr29ravlfTi1bArAjQAJ4EcDbDMJbrr7/+gDbg559/Hn19\nfejr68Pjjz+Oq666qvYrLJL3+71ot3OSNUzVukFsKghDwGt5gBQsIw9xiGoz1ucwnVqYlGjMGhhb\njPD3+WW3k/Yu3oiRpZ+HY8sf0fTvn5T8ekIIzG1mSTrXjZqCsUYql8PFT76NaKp2piRnnnkmdDrd\nSkJIR80uWgGzIkBTSv9mMpnONplMRKM5UMzj2WefxUUXXQRCCFauXIlQKISRkZE6rHR6BodGMBDJ\nYU6TNHPAbCoCdXQECXttG8T2ZyytrbVo0by0ua5+0rMNQg7toqci4UuA5dkZ1cKkRDAJhZ20DGvS\nwysugW/uaWh+9ynYN/++5NfzGh5qgxpxT/la3WO4rBxuXGfCNk8MX/nFe8jV6L0ihOCmm24S3G73\nAzW5YIXMigBNCFELgnC53W6fNIc1NDQEt9s9/n+Xy4WhoaGara8Y8vk8Xn23Bzlxcg/octD6egEU\nrOnqBaUUgZ0BqPVq6Bv0dfOTnq0cahSbnFwqh5gnBpO79qUdjUUDfYO+sJOWU5AmBLuOuxFh99Fo\n/cf3YN7595JPYWw2Iu6LI5+p/DN3eKsal6014tVeL771p56Kz1csF154IcPz/HpCSEPNLlomsyJA\nGwyGi0866SRFqoaNsW3bNsRVZgBAu0Oan0Pn3Rug69QgNmZIwAkcDE2f1pwPBWnpOCRWciCVqIVJ\nhdaqhdauRWC7zOakGQ47Tr4Dccc8dPzlW9CPfFDSywlDYGwxIjxY2Wz0GKfuNdZ48o1+/PSf/ZKc\ncyZYlsXVV1+tbm5uvqsmF6wAxQdoQggxGo13XnvttVPOT7S0tGBgYGD8/4ODg2hpaanJ+oohmUxi\nZGQE/pwAngWazRLtoL1bkTK5kFeXbk8pBdHhQkf6ZN3ah4K0NOwv90kphZgXkc/mkUvnkM/mkc/m\nIeZEiHkRVKSyU7+SmkrVwqRCZ9dBbVRX5LFcDQpqY/cgo29E54u3QwjsKOn1gklAPpdHJiaNcchF\nawxY3qHGnb//BK/VyFjjmmuuYVmW/QIhRNai/4oP0ADWLl261NjU1DTlAWeeeSaeeuopUErxr3/9\nCyaTCdMdX2s2b96MBQsWoGdPFG4rD46Vptu0WhaTxRDzxJBNZmFum3rO+VCQLh5KKXKpHFKRFOK+\nOMJDYQR2BpAMFPyKRz8Zxegno/Bs9sDX60NwZxCh3SEEdwYR2BGAf5sfvl4fvFu88Gz2jB/v3epF\nYEcAod0hRPdEkQqnkEvnZBVQSkEqtTCpMDQawHCMZDtOqcgLJvStvx8iJ6D7+VvBx0aLfi0hBGa3\nGaEBaaROWYbghtNMcNt4XPPzf6O3BsYagiDg7LPPVhsMhi9X/WIVQJT6hzhGR0fH2x0dHct7enrg\n8/nQ0NCAu+66C9lsoTPwyiuvBKUU1157LV544QVotVr8+Mc/xvLly+u88gKBQAC9vb1YuXIlln/r\nZRzmZnHtKZXXzbhkEIf/9BwMrLwKniXnSrDS4kmGkojticE+116U0IqYE7Hnoz1QG9WwddpqsEJ5\nQ0WKbDKLTCKDbDyLbCILSik4NQdWxYJVs+BUHFg1i0w8AzEnlhWQKKWFnXVWRD6XRz6dRzaVRS6V\nK9QYSUHukdfyBclZnUrWim9iXoR3ixfWTqvkgiSVQClFcEcQKr0K+ob6ZLOmQuPfjrl/uAFZrQ1b\nz3ykpHHM0K4QVHqVZM5zvmget/7SD51ahf+9Zg3sVc6ADAwMYNWqVaODg4NNVKaBUNEBmhDSunjx\n4p6PPvpI1mmKqaCU4vXXX8fSpUuRBI+jvv0KvnysARuOqFxQwbj7TXS/8HVsPeNhxJoOl2C1xZFJ\nZBDcGYRjngMMV3yC5mAO0lSkyMQySEVSSEfToCIFr+Wh0qrAa3nwWn7KWmo6kkYylIS51VyVdeXS\nOWQTWaSjaWQSGRBCoNKpoDaooTKowHLycXMN7gqC1/DQO+UVBIHCe+nr9cHQZIBgEuq9nH3QD3+A\n7udvRsLWjd4N3wHlilufmCs8EDkWOCSr9W8bzeKO3wawqNmEX1y2EkKVx+PWrVuXfOGFF06nlP6t\nqhcqE0WnuF0u1+1XXHGFvD7tJTA8PAyTyQSDwTCuINYmYQc3BUHC1i3J+Yohn80juDMIa6e1pOAM\nHHzp7lwqh+ieKHy9Pnh6PEgEElDpVLB329GwqAHWDiv0DXqoDeppb37V7OImDAGv4aG1aWFpt6Bh\nYQPs3XYIJgGZeAb+Pj88WzyI7okil8pVZQ3Fkgwlq6oWVimEIbB2WhEeDCObrN3cbzHEmg/HzhO+\nAZ2nB3P+fHfRamMMx0Dr0CI2GpNsLV0NPL5yignv7g7hlt9+WPVSy9e+9jVNe3u7bEeuFBugCSFq\nSun5l1xyiSJ/BlEU0dfXh7lzCzXinr0a3O0OaVJzOu9WpMytEFW1SS5QkcK/zQ+T21R2enG2B+lc\nphCUPZs9CO0OgWEZmNvMaFjUAEu7BRqLpvQHG56pqSgGwzEQTAJMLhOcC5ywddrAsAxCA4U6eHgg\njExCmuahYsln84gMRqquFlYpLM/COseKwI6A7EbjQnOOw+4118G8+19o+/t3i1Yb0zv0SAaSkrqq\nHdMt4PPH6PHsaj2PWAAAIABJREFU+8P43ivVNdY48cQTodFoDiOENFf1QmWiyOAGAIIgfPa0005T\nTyZMogR2796NhoYGCEIhAdAzEoFdz8IgSPMr0Xq31nT+ObgrCK1NC8FYWUJjtgVpMSci5onBs8WD\n4I4gCENg67bBPtcOnUNXsf81w9Y2QO8Py7PQOXSwd9vhmO+ASq9CdLjwEBIbjVV9beNqYa7aqIVV\nCq/hYXKZCjaVMisv+haeheEjL4R96/NofudHRb2GMAT6Rj2iI9I2dm1crsPaBQIe/nMvfl9FYw1C\nCC666CJ1Y2PjDVW7SAUoNkA7nc67brjhhsrdJOpALpfDzp070dXVNf61zcMRydLbfNwHVcKPeI0s\nJuPeOEAhWXpxNgTpbDKLYH8Q3i1eUJHCNscGx3wH9E69pIFETjtGhmWgsWhg67LB1m0DpRTerV74\nt/uRiqSqEpDG1cLMynlQF0wC1AY1IoOVmU9Ug5FlX4J3/no0vfdzOD5+pqjXaG1apCNp5DLSlTkI\nIbjyBBMWNqvwtV+/j3/vqp6xxpVXXsmyLHsZIUR2T3iKDNCEkPkNDQ3Nhx12WL2XUhY7d+6E2+0G\nzxdSwalsHjt8MUnrz0BtBEoysQzi3vi041TloMQgTSlFMpiEd6sX4YEwBLMA5yInDI0GsKrq/u3L\nbTfG8iwMjQY4FxZ+/qQ/Wai1+xOSrbWeamGVYmgyIJvMIhlI1nsp+0IIdq/5T4TaVsH9xvdh3vFq\nES8hMDYbER2SdhfNcwQ3/YcZVh2Dy596BwOB6hhrmM1mrF69WsMwzOlVuUAFKDJAu93u26+88kpF\nNodls1kMDg6io+NTrfZtnhjyUkp8entBCYOEvWvmgytAzIkI9gdhnWOtimKTUoI0pRRxXxyezR6k\no2mY28ywz7VDY9bUZIfLcPVNc0/HWNe3pcMCe5cd2UQWnk/2pr8rkMGUg1pYJRBCYJ1jRWQkgmwN\nzSKKgmGx46TbEW9YiI6/3Av98HszvkQwC+MjelIy0Vjjkp9Uz1jj+uuvV7vd7nuqcvIKUNwnmxDC\ni6J45gUXXCCf3F4J7Ny5E62trWDZT3dUm6vQIJa0tBc9LlEOE52COKF6EqtyDtKUUiT8CXg2e5BN\nZmGfa4e51VzzGVyl+EKzKhYmtwmO+Q6IoghvjxfRkWhZUphyUQurBIZjYGm3ILgjKC85UACUE7Dt\ntG8jbWpG14t3QOPfPu3xhBAYmgyIjEiftndZOXxtvQnbvTFcWyVjjVWrVkGr1XbJTZ9bcQEawCnH\nHXccr1Ipr/ycy+UwNDSE9vb2fb7eMxKBmiNoNEmQBqUUWl9v1dPbcW8cDMfUxClIbkGaUopEoBCY\nM/FMITC7zXVrUmI4ZRlmMBwDY5MRzoVOgKDk1Lfc1MIqQaVTQWPVIDwkL6UxAMgLRvStux95lQbd\nz90CVXTPtMcLJgG5ZA65tPQjd0vcBWON16porHHuueeqrFbrl6py8jJRXIDu6Oi49ZJLLlFUevuF\nF17AvHnz0NnZieeee26f3TMAvLfTi3xgABee/gWcf/L5eP2V18u+Fh/3gk8Gkahig1g2mUXcF6+K\nOMZUyCVIZ5NZ+Lb6kI6kYe8u7Jjr3T2sVMMMwhAYGg2wz7MjE8/Au8WLdGR6r3AxXyirWDosslY1\nKwV9gx65ZA6pcKreSzmArN6JvnX3g+Qz6H7uZnDJ0JTHju2io3uqI9V5ymItzjyiYKzx1D/7JT//\npZdeyul0uq9IfuIKUFSAJoQYKKVHnnDCCfVeStHk83lcc801+OMf/4gf/vCHePHFF7F58+bx71NK\n8fFgEG02Dr946Re45wf34P7b7i/7ejrvVgBAvEojVlSkBTGSdmvNb5D1DNJiXixoW/cHYXKbYGm3\nVL3xq1iUbjnJcizMrWZYO6yIeWLw9fmm7AgOD4ahc+hkJeVZKYQQWDosCA+EZfl7TFk7sO30b0MV\n86DrhdvAZKdubBPMBREbKewoJ+OC1QasmFMw1nh1q0fSc7vdbjQ1NVkIIbVTd5oBRQVotVp9zoYN\nG3iGUc6y33rrLXR1dYHjOLS3t+Nzn/scnn322fHvj4RTyDEqaHIBAEAsEoOjwVH29QoNYiyS1s6K\n1z4ZkeEINBYNeG19bpC1DtJjdWZvjxe8hi/M+urkVV6Rc5NYKXACB1uXbdxLOeaJ7ZP2lrtaWCWw\nPAtjixGh3VPvUOtJvPEw7Djpm9D6ejHnz3cB4uQPUIQUsiLV2kWzDMH1p5rQZudxzS/exVaJr3PR\nRRdpmpqarpH0pBWgnEgHoLGx8ZYrrrhCXnfHGRgaGoLL5cKuXbvQ0dEBl8uFoaGh8e+PKYj1/O1l\nrF+2HtdfdD1u+tZNZV9P69uKpLUDlJP+bcrEMkjH0tA31lfruFZBOp/Nw7/Nj3Q0Dcd8B3QOnazm\njsdQaop7KgSjAMd8B3KpHHy9voJ5h0LUwipBY9GAgCBRpXGiSgm3r8buNTfANPAW2l/7zpRqYxqL\nBuloumoPjRoVg69vMEPNARc/+RZ8senLIqVw4YUXEoZhLiIy+ZApJkATQhp1Ol3r4sWL672Ukkkm\nk7DZbONzzxMZC9DrTlyC5/79HB556hHccd0dEMUyPtyUQuftrYpACRUpgruCsrlBVjtIJ0NJ+Lb6\noHPoYGm3lCzBWUsYnpFUalEOMCwDc6sZxhYj/Nv98G7xwtBiqHu9v9qY28yIDkdlmeoGAN+CDRhe\n9iXY+l5Cy1s/nPQYQgh0dh3ivnjV1mEzsLhlgxm+WBqXP/UOUhK9X0ajEYsXL1YDOEqSE1aIfO86\n+2GxWL68adMmRe2eAaClpQXbtm3DnDlzAACDg4NoaWkZ/37PSBSI+bDuzEJdfcnyJcikMwgFSk91\nqaJ7wKUjVWkQiwxHoLVpZVX7q0aQHmtCinvjsM+zK0KhiuVm1w56Imq9ejxzkfAmZBu4pILhGBhd\n8k11A8DIkRfCu+AMNH7wNJwf/XbSY7R2LRI+6URpJqOrgcd1pxaMNW7+jXTGGl/+8pe1bW1tX5Pk\nZBWimACt1+uvvfTSS6s3cFslurq6MDQ0BI/Hg0wmg6effhpnnnnm+Pc3j4QhZPx4+/W3AQA7+3Yi\nnU7DYrOUfC2trzoNYtlkFulIWnZetoC0QTqTKHQSq3Qq2LpsitmtEYbITklMKnKpHOLeOBwLHNA5\ndYUO+qh0KU05ojFrAApZdnUDKKiNrb4OwfY1cP/zUVi2/eWAQxiWgdqkrrpS2souAV9YpcfvPxjG\n//1znyTn3LhxI/L5/GmEkLrvRhQRoAkh85qbm80Td55KYdeuXXjooYdw2mmnYcGCBdi0aRMWLVqE\nO+64A79+5vfo9ydw7FEdeOYXz+BzJ38O37j6G7jz4TvLSiPrvL0QGR4pS8fMBxcJpRShXSHJpTyl\nRIognfAnxq0y5VprPtjYXy1MY9bA1m1DeDBcEDiZpQ8lAGBuNSM8GK5Iba2qMCx2nng7oo2Hof3V\n+2AYeveAQ/QN+gMa/arB2ct0OGGBBo+80odn3x+a+QUzwPM8jj32WBWAUytfXWUoYkfqdDq/dO65\n58o/17gfqVQKiUQC5513Hs4///x9vnf33Xfjvd1B0DffwBHznLjy2eLcY6ZD692KpG0OKCvdg1/C\nlwCv5WXXubw/Y0F6z0d74N/uh63TVtTrKKWIDBbkFh3zpTOerzWEIRDzomLXPxnRkSgEg7CPWhin\n5uCY50B4MAz/Nn/VZGbrDatiobPrEB2OylZrnHIqbD/t25j3++vR+dId2HrGw0jaP51Q4lQcWBWL\nTDxTVcU3QgiuONGI0UgeN/3mA7gsGixrs1Z0zgsuuED95ptvXg/gT9KssjwU8clWq9UXnH/++Yrb\n0uzatQttbW1T7sZ69lq0SaLBTUVofdI2iIk5EbHRGIwtylBsKnUnLeZE+Pv8AAPYumyKvtHPtk7u\nMbUwQ7PhgO8RhsDcaobGooFvq69qM7f1RufUIR1Ny0+rewJ5tR596+5DXqVH9/O3QhXZ1xpS79QX\n3O6qDM8S3LzeDJuexWUSGGuceuqpSKfTK+vtcCX7OxIhpNFsNluVlt6mlGJ4eBjTrbtnJAKtisBp\nrPwzoI4Mg8vEkbBLF6AjwxHoG/WKClzFBulcOgfvVi90dh1MLSbFp7QZbvZ0cu+jFjbN70Vn18Ho\nMsLX50M2Kd8gVi6EEJjcJoQH5CcDOpGs3oG+9feDiHl0P3cLuOSn1pAqvQrZRLYmc/oGDYOvn2FG\nOpfHxU++jUgFDzYcx+GII47gUOdubtnfebVa7Vnr16+Xd351EjweD2w2Gzhu6t1xz0jBA1qK4KDd\nqyCWkKhBLJvMIhPPQGvTSnK+WjJTkM4ms/D3+WFps0BjVVzlZFJm0w66FLUwwSjAOseKwI4AUhGZ\nNlVVgNqgBmGIfBvG9pKytBXUxuI+dD3/dTCZwg6WEAKtTYuEvzaz3S0WDjetN2GHL4Zrf/5uRcYa\n55xzjsblcn15umMIIXlCyPuEkI8JIb8mhLTs/f/7hJA9hJChCf9XEUK+QQj5hBDy4d6vHT3d+WUf\noBsaGq7+/Oc/r4ha+UT6+/sPMMWYiChSbB6JoM0ulYNVL0RWhaRl6muWQnggDJNbuTvLqYJ0Jp5B\nYHsA1k4rVHrFPfdNyWyZhS5HLYzX8LDPtSMyFJGtyEclmFwmhAfDsnO82p94wyLsOPkOaP3bMOfP\nd4LkCzvYsZnoWjX1HeZW4/K1Rvytz4e7/7h55hdMwcaNGwHg7BkOS1JKl1JKFwPIADhv7/+XAngM\nwMMT/r8MwAYAR1JKlwA4GcDAdCeXdYAmhGgopZ1Lliyp91JKIplMIpvNwmicunY7EEwgkclL5wHt\n21rwf2YqT5enIikQlijayg84MEino2kE+4OwddvAa+o+QSEpDMcofgddiVoYy7Owz7Uj5onVbLdW\nKzg1B8EkVFX4QyrCbcdg17E3wjT4DtpeexCgIhiOAa/hkYllaraOkxdrceaRWjz1z1148h87yzqH\n0WhES0uLhhAyp8iX/B1A1zTfbwLgo5SmAYBS6qOUDk9zvLwDNICTjj/+eGUMo05gYGAAbrd72mPG\nFMQkCdBiHlpfnyT1Z0opIkMRmFrk2TlaKmNBOhVKwbvFC3u3HZxacQmZGVF6invcX9xlLHv+nGEZ\n2LvtiHlnX5A2NBoQ98TlO3Y1Af/8dRhafjFs2/6Mljf/GwCgc1RXWWwyLlhVMNa4+4+b8dcyjTXO\nOusswWq1bprpOEIIB2AdgI+mOewlAG5CSC8h5AeEkONnOq+sA3RHR8dVn/3sZxVlLTnWHNbc3Dzt\ncZtHomAI0GqrfCcnhAfBZpOSdHCnginwGh6cMHuCWDaZBatiQViC0IB8FZoqQekp7oQvAZZnK1Zu\nm61BmuEYaO1axD3y30UDwJ4jvgDPwrPQ+OGv4fzwV4VmsXi2pmn6T401OFxbprHGpk2bWIPBcOk0\nh2gIIe8DeAfAbgBPTHUgpTSGQpr7cgBeAL8khHxpuuvLNkATQphMJrPm1FPrPiteEuFwGHq9flLd\n7Yn0jETQZOag5uXRIPbGX9/AxmM3YtNpm/DMs89MeszLv38Z5649F5tO2IRvXPONsq9VSzKJDEK7\nQ7DPs6NpSVPd/aSrhZLlPnOpHGKemGTzvvsE6VlUk9Y79Uj4E8pwLiMEA6uuRbDjOLj/9Rhs2/4M\nwSwgGayustj+FIw1LOPGGt4SVeg6OzshCEIjIcQ8xSFjNeillNKvUEqnzeNTSvOU0lcppf8HwLUA\nzpnueNkGaADLFi9ezKtUymrkGRgYgMvlmvG4npEw2iSrP/cizwlImaZPq09FPp/H/d+4Hw98/wH8\n6Jc/wst/eBk7enfsc8zuHbvx4+//GE/87xP41V9/hRvvulGKpVeVbCqL4I4gbJ02cCqurn7S1Yaw\nRBHpz/0ZUwuztFkkHecbD9J7YrNGGpQwBDqnDrHRWL2XUhwMi50n3IZo01K0v/oAmtI9dclq2Aws\nbj2jYKxx2VNvl2yscdJJJ6lYll1X6ToIIfP285peCmDXdK+RbYBuaGg474wzzlBUelsURfh8Pjid\nzmmPi6SyGAymJGsQ03m3ImHvLrtB7JP3PoG73Q0DZ4DVbcWpZ52K1158bZ9jnvnFM9j0pU0wmguN\nb1Z7ZUo91SaXySGwPQDLHMs+6frZGqSV2m0/phZWjY56hmVg67IhtCs0a+akdXYdksGkYh7GKKfC\nttPuRtLShrmv3Q19qK8uhiedTh7Xn2bC+wNhfO3XH5TUUb5x40a+ra3tcgmWoQfwE0LIZkLIhwAW\nArhzuhfINkALgrDxjDPOUNRdx+v1wm63g2Gmf1u3SKkgJuah9W2ryMHKs6cws602qMHyLJxNTnj2\n7NtUsXvHbuzasQsXn3UxvrThS3jjr29UuvKqIeZFBLYFYG41Q6U98MY/W4M0CBSlT52JTa0WJhWs\nih2fk54NTliEIdA5FLSLBiCq9Ni27j7kBBNWbH4A2L29Lus4ulPABav0+OOHI3i4SGMNSimOOOII\nZDKZ5ZN5RFNKp3QQopTeSSn9zoT//5tSuopSupBSuoRSupFS6pvu+rIM0IQQgWVZZ2tra72XUhLF\nNIcBEzu4JWgQC+0Ck09X1iBGgWwiC0Pj1DfKfC6PgZ0DePw3j+PbP/g2vn3TtxENl950UW3GuoF1\nDh3UhqnHxGZjkGY5Vhn1SexVC9s1s1qYFPBaHiaXCf5tfsXsPKdD59AhGVDOLhoAsjp7QW2MUCz+\nxx3gEoG6rOMzy3Q4caEG33ulD0fc/RI6bv0TVt/3F/zve5+abKTTaQwODuK9997Dq6++im3btsHl\ncnEApHMhKhJZBmgARx155JFyXdukiKKIUCgEq3Xm1G/PSAQGgYFVX/mPqBtrELOX3yBmNprhD/jB\nqgopcs+IB87GfdP0ziYnjjv1OHA8h5bWFrTOacXunbvLX3iViO6JguXYooQuZluQZnhGMbvE8EAY\nOmdxamFSIJgE6Ow6BPuDisoyTAZh9qpz+ZTVAJc2t2LbunuhyoTR9dwtYDK170gnhGBhCw9CgGAi\nCwpgKJTErb/9EN//w5t47bXX8M477yAej6O9vR1r167FUUcdhRNOOIFTqVQn1Xq9sgyCTU1NZ518\n8smK0mD0+Xyw2WxF7QY2Syrx2Ys8r0PaVL5WucvhwsjICIZ2DyGbyeKlZ1/Ccacet88xa09fi3+/\n8W8AQCgQwu4du9HSKi999GQoiXQkXVI38GwK0koRK0mGkshn89DZi1cLkwKdQweGZWpi3lBtxuaK\nlfawkXAuwOYVt0AT7Efny/8HJF878ZIxnv5XDPu/bamciJ99GMHq1auxevVqzJs3DxbLp9md008/\nnXO73V+o9VplGaBVKtXZ69evr/cySmJkZARNTU0zHpcXKXpHo5I2iMUd3QAp71eZTWTBcRxu/vbN\n+Mrnv4LPrv0sTj7jZHTO68RjDz6G114qNIsds/YYmCwmnLv2XFxx7hW47pvXwWydavKg9uRSOUSG\nIrDOsYIwpT34zJYgzfLyT3FXohYmBeZWMxK+BDLx2gcGKWE4BmqDGqmQvDW6JyM5fw0+nnsFjEPv\nov2v9wO0+p/ZsVE+/zY/fNHJrzcazUzpnbBq1SqkUqmlk9Whq4ns1CgIIcKcOXOcMylxyQlKKQKB\nAA477LAZj93piyOVFSUZsSL5LDSB7fAs2lj2OaKjUegb9Vgzdw3WnLRmn+9dedOVn16LEHz1zq+W\nfZ1qQkWKwM4ALO2W8lWoyvSTlhMMJ+8UtxRqYZVCGALrHCv82/1wzHOA4WS5RykKvVOPYH8QGoui\nko3g1ByGHMfBYkjD/c4TyGotGDzmGkDC2CfmRKQiKaQjaWTiGbAqFoJRgLHFCKsuiED8wCDdPI1I\nDsdxaG1t5YaGhjoA7JjyQImR46dTcfXnYDAIs9k8Y/c2IHGDWLAfTD5bdgd3PptHNpmdtplKCURG\nIhBMAlS6ykZ1lL6Tlrvcp1RqYZXCCRwMTQbF16M5gQNhCTIJ5WUDBIuAne6zMLp4Ixo+/h0aPni6\novNRkSIVSSE8GIZns2fcglRr08K5wAl7tx36Bj0YNQet6sAHAQ3P4qbTpr+Prl27Vl3rOrTsAmFj\nY+NnTjnlFEU9Eo6OjqKhoaGoY3tGImAZwG2tfAc91iAWL1NBLOFLQGfXKXaGFgDSsTTS0TQMTdKM\n6ig5SMtZ7lNqtbBK0Vq1YDhGcY1W+6N36hUj/zkRwSQgFUlj8JirEeg8Aa63fghr74tFv55Sikwi\ng+hIFN6tXnh6PEgFU1DpVbDPs8O5wAlTi2ncrnOM374dx2Awj/OPcqPFrAEB0GLW4N6Nh+EzR0zf\nU3P66adzLpfrgnJ/5nKQXYpbrVZ/Zt26ikVbaorX60VX13QmJp/SMxKBy8qB56RpEMupDcgYZq59\n7w+lFIlAAo55jorXUS/EvIjQrhBsncU15xWLUtPdcpX7pHRvCUJitbBKMblN8G7xQjAJ4xMMSkNt\nVCM8EIaYF2X13s4Er+GRS+ZAQdC/9hZwqTDaX70frn/9N7hUGBm9A0MrLkWw++Tx1+TSOaQjaaQi\nKeSSOfAaHmqTGpYOCzjVzKHso4E0fvVmDBuPbMG9Zx9W8j1jbx36cEIIoTVKvcjqN0oIUTEMo6j6\ncyqVAsuyM2pvj7F5JII2m5QWk3PLqt2ko2motCpF1+DCA2HonfqqGHsocSfNcPLcQUdHohCM1VEL\nqwSGZWBymRDcpdxUNyEEGptGccYghBCodCpkYhlQVoXAnBMAEPCpEAgo1DEP2v72EHTvP4fgriBG\nPxlFcFcQYl6EockA5yInrJ1W6Oy6ooJzMJ7H/30xjE6HDt/6zOKyHuj31qF51HAeWm5358Xz589X\nVL51dHR0RmnPMYLxDEYjabQ7Kq8/k1wGmsDOsgVK4t54UbPCciUdSyOXyUFr11btGkoL0oQhgMzi\nTC3UwipBMAlgOKbmJg5SorPrFJmqF8wCUuFCF3rTez8F2e/Dy+bTaPvwJ9BYNHAucMIx1wFDowEq\nraqkAJsXKR55MYxUFnj0C8ugLSKgT8WKFSt4AMvLPkGJyCpAq1Sqo5YvX66ojiWPx1NS/RmQRuJT\nE9gBRsyV5WAl5kTkUjnwutqIREgNpRTh3WGYW81Vr58rLUgD8pH7HFMLs3ZYZd3nYHabER2OyjL7\nUAwsz4LhGcXpjasMKiRDSUT3RKGKeSc9Rkj5IBiFkkcnJ/Lbt+P4aDCDu89ajHnTqCUWw9FHH827\n3e7TKjpJCcgqQLtcrg2rV69WTDGIUopYLAaDobhf+mYJA7TWN9YgVvoOOhlMQmPVyPqmOR1xTxxq\no7pmKlRKCtKEJTX13J2OMbUwuXuLMxwDQ7MB4YFwvZdSNlqbVhFp7nwmj7gvjsCOAHxbfBCzIigo\nMrrJe2Ey+sp6ZCbWnc9dNrPL4Ewce+yxYBjm+IpPVCSy+svJZrPLVq1aVe9lFE0kEoHRaCw60PWM\nRGHRsTBpK38G0Xl7kRXMyOqKS69PJOFPwNJhqXgN9SCfLfyBO+bXtrlNzo1jYk5ELp1DLp2DmBMR\nHgiDEAJRFAspb1Ko+TEsA4ZnwPIsWBULXsNXrQdhTC3MbJePmM10aCwaxD1xZBKZSQ1W5I7GrEF0\nOApjS/H3o1og5kWko2mkI4VpC4ZjoDaqoW/Qg9fyiAxHwKk4DC//ItpfexATV57n1BhacWnZ15ai\n7rw/ra2tEEXRWatGMdnsoAkhKp7nDcXuRuWAz+eD3W4v+vjNI2G02aRJEGi9Wwvp7RI/dLl0DiAF\nsQAl8cZf38DGYzfi7NVn4/fP/X7KjtVX/vQKlrcsx+YPNku+BjnspCmlyMQL4yX+bX6MfjIK3zYf\n4p44cqkcGI4Bq2ahsWqgb9DD0GiA3qmH1q6F2qguNJJl8kj4E4XXfzwKX68PkeEIUpGUJLvvequF\nlQMhBCaXCeFBZe6iCUOg0qvq7n1NKUU6lkZkOALvFi98W31IR9MQTEKhjjzPAWOTESpdoY6sNqiR\njqahjo6CAMhqLKAgSOud2HXsjft0cZeClHXn/XG73Qxq1Cgmp7v0orlzyzd8qAc+nw+LFi0q6thM\nTkSfJ4YNSytvaiK5FDTBfoTaV5f82mQwqTjloXw+j/u/cT8e+dEjUOVUuPGGG3HSWSdhztw5+xwX\nj8Xx9BNPY/ERi6u2lnrspMdEGJL+JLLJLHgtD7VRDZPLBFbNHhAEeTVfkvhMLp1DJpZBMphEeCAM\nTs1BMAvQWDQlj+7IQS2sXFR6FRiWQSqcgmBSlBU9AEBj1SAZTEIw1m7tlNJPx5/CKeTTefA6HoJR\ngK5TN+NnQK1XI71tOxo/eBqBOWux8+Q7JFnXWN35gc8uqbjuvD/Lli3j33jjjWWogaKYbHbQKpXq\nqBUrVijmr4JSikQiAZ2uuE7o7d4YcnkqTf3Zvx2EimU5WCkxQH/y3idwt7th4Aywtdlw6lmn4rUX\nXzvguMceeAxfvPqLUAnVTVHWaiedTWYR3BWEZ7MH6Uga+gZ9Ybxkzt7xEuFAw5VyRq04NQetTQtL\nmwXOhU4YW4zIZ/LwbvEisDOAdDRddOOZXNTCysXkMiE8FJZNo10pqA1qZKKZqq89ny1kYAI7A/B8\n4hmv3ZvcpsLns8MKrU1b1AMaYQgW7PoFAGBw5RWSrG+87nyENHXn/TnmmGNUra2tp0t+4kmQTYB2\nuVz/ccwxxyjmkbv0+nOhQUwKDW7tmMVkiQ1i+UwehBDF7Ww8ezxwOB2gIoXaoIazyQnPHs8+x2z5\naAv2jOzBmpPXTHEWaalWkKaUIhVOwbvVi/BAuJAaXOSEudUMlX7m8ZJK5T4JIeA1PIzNRjgXOqGz\n6xD3xuHd4kUikJj25i83tbBy4NQc1Aa1Ihqu9ocQAl7HIxOTVvqTioXPZGgghNHNowhsDyCXzkHn\n0MG5aK+zouc4AAAgAElEQVSMplMPXuBLLmnoRz5Ak/efGJh7DrL64qZhpmOs7jzHocN/SVR33p/V\nq1eDYZjjZj6ycmST4s5ms8uPOeaYei+jaAKBQFHez2P0jETAswQtFikkPnuR0dqQ1RVf/waUuXse\nIxPPwNhsnPR7oijiu3d9F3c+fGdN1yR1ujsdKdTuWBULc6sZvKb0LnUp5T7HaoRqgxq5dA6x0Rii\nI1EYm40QzMI+Nz+5qoWVg6HRAF+vD1qbVjE19DE0lkKauxJ9fUopsonsuNmEmBeh1quhNqphbDZK\n9/sV83C/8SjSWgd2uM7E5H/dxZMXKb73UqHu/IMvLIOuSn02ra2tyOfzDbVoFJPFXxIhhOc4zmg2\nK6PjEygYZFgsxXdC94xE4bZxYCuY5xtjXEGsRJKhJASzYqoI41hMFni93nElKs+IB87GT7vXE7EE\ntm/Zjis+ewXOOPoMfPzux/jql79alUax/ZFiJ51L5+Df5kfME4O5zQzrHGtZwRmontwnp+ZgbjXD\n3m1HMpSEb6tvH5MGuaqFlQPLs1Dr1UgFlWflKBgFpCPFlyTGyKVziHkLdoyeTzyIjcbA8iysHVY0\nLGyAudUMjbn0noTpsG99Hlr/NgwedTlSqcrvi799O44PBzK4+8zK551nwuVysQDaq3oRyCRAA+jq\n6OhQ1KPqWIq7GCil2DwSlqT+zGSTEIK7S05vi3kRYk5UXPc2ALjsLuwZ2YOh3UPIZrJ46dmXcNyp\nn2aY9EY9Xvn4FfzhzT/gD2/+AYuPXIzv/vi7WHj4wpqsr9wgTSlFdE+hG1vn0MHWZSs7MI+vha+u\n5SSrKty0Ta0mhHaFEB4MjzcIyVUtrBz0jXpER6OKq0UThoBVs8ilctMeJ+ZEJAIJBPsLMpqhXSHQ\nPIWxxbhPn0O1NMrZdAzNbz+BaONhCHWfCCrSiiYI9qk7L5e+7rw/ixcv5gBUrxt1L3K5W8+dO3eu\nXNYyI9lsFizLFmUvCQDeaBqBeBZt9sp3r1pfHwhoyQ5W6WgaaqOiRNoAFOqaDGFw8z034yuf/wry\nYh5nnncmOud14rEHH8OCwxfg+FNrphswJaWmu3PpHIL9Qai0KjgXOCtSSpoIYWojVKLSquCY70B0\nTxS+Ph+snfJWCysVTs2BU3OFEaEadkVLgWAqSGhOfNijYmH8KR0p/AMpGG1obdqCIp9En79iaXr3\nKXCpCAZWXQvs7XvIJrNlWcbWou68PwsXLlSZTKYlAP5QzevIIihardalCxcuVExurNT0tqQKYmMN\nYiWmuFOhFDRW5dWfY54YdE4d1sxfgzUn7dsAduVNV076msd/83gtlnYAxQbpZCiJyGAE5jaz5F7c\ntQyShBDk03kYGg2IDEcgZkVo7cqr206FodGA0EBIkQE62B+E2qged38SsyJUehUEowBDo6GuJjnq\n0G44P34GvvnrkbR3AyiMuGXimZIDdK3qzvuzePFiOByO0udcS0QWKW6r1brqsMMOq/cyiqac+jMA\ntNsrl6bU+nqR0TmR0xbfoAYUmqzUemXtoMW8iHQkrajGtunS3ZRSREYiiI3GYJ9nlzw4j0FIbXbR\nY2phhmYDHPMcSEfThVSpwtLCU8FrC13JStG4zmVyiPviCA+GkYlmEBmOgGEZWNosaFjUAEubpTDb\nXk8HO0rh/ucPkOcFDK+4ePzLKl0hQJdKLevOEzn88MORzWarXkOTRYDOZrPzlRSgw+EwTKbiR0l6\nRiKwG1johcrfbp13a8np7Vw6B5Zna57GqpREIKFIzfDJgvSYgEc+k4e9217VUTeGYyDmqusLvb9a\nGMMysHRYwKpZ+Pv8EPPy86UuB51dh7gvXu9lTIqYF5EMJhHaFSrYMe4MQswV7Bi1Di30Tj10Dp2s\n+k6MA2/CNPAWRo68CDnNp5scXsMjmyjtQajWdeeJOBwOiKJY9a5mWfzmRFE022zy0TaeiVIESoBC\ngJbCA5rJxCCEB+GfW5qZSjqartpurVpQShH3xmHvLm2UTC5MTHf7tvlAQMBreRiaDNV34NrbKFat\nBp+p1MIIITA2GZFQJeDr9cHebVe03zhQsESMDEdAW2jdH3CpWJB5HRt/Ai3UkQWLAJPbtM/68oa8\n7OrnJJ+F+58/QMrkhnfRZ/b93t61U7G497kedef90ev1HCFETymNVesadf/rIYQYjUajYpQzcrkc\nGIYp+gORyuaxwxdDu0MKB6s+ACivQUxhATqbyIJTc4oTVZkIwzFoWNSAVDCFTKIwx12LGwnLs1Xd\nQc+kFqa1aQuzxH2+qu/kqw1hCNRG9bhvcS2hlCKbzCI6WmjE8/R4kPAnwGt42LvtcC50wuQyTWrH\nqDLUX5d7f5wf/w5CeBADq64BZQ8s93ECV/AKmIF61Z33p6OjAwC6q3mNugdoAN1z5sxRTA4zGo0W\nbS8JAH2jMeRFaerPujIbxLKJrOK8nxOBBLTWynXL6wmlFKGBEIwtRtA8rZnBBsNVb9SqWLUwjUUD\nQ9PeIK3wdHct09zjMpo7CjKakaEICCEwu82FOnK7BVqrdsbMBMuzoPnKRpekhEsE0PTuTxFqXYmI\n+6hJjyk2zV2vuvP+zJs3TwWgqgYSckhxz507d65iOrhLmX8GPpX4lGQH7e1F2tCEvFB8/TufzYPh\nit/xywFKKdLhNEwtypWMBIDQ7hA4gYOx2Qi9U18zgw2WZ5HPSB+gS1UL05g1EHMiAjsCsHXZFPUZ\nnAiv5SHmxELZQOKMzkx2jJW8Z2ONV3LInrW8/QRIPoPBlVdNeQyvnVmm9KOBNH79Vn3qzvuzYMEC\nvqmpaQWAX1brGnUP0M3NzUctXLiw7usolmg0ioaG4jVjN49EIPAEjSYpPKBLbxArZ3Sh3mRiGfA6\nvu41v0qIeWIF4YfWwsNcLV2wGJ7ZR+VLKqLDUQim0tTCdHYd8uk8wgNhmFuVoxS4P2MSmnqnvqLz\njNmFjs0ji6IItUENwSTA2CKhjCbkE6C13q2wbX0Bo0vORdrsnvI4XsMj7p06UxFK5PHIi2F02OtX\nd57IokWLoNVqJ08HSETdU9wajWbZ/Pnz672MoolGo9Dri/8j7RmJoNXGganww8SmIlBHR5Cwl6Yg\nlokpL0ArPb2djqaR8CcO8EOulQtWNeQ+M7FCc5KhqfSUoqHZMO5BrVQ0Zg1SodLr0JTS8bLAmIxm\n3BsHp+Zg7dwro+k2QzAJkmuYj80W1xVK4X7j+8hpzBg58oJpD2VVU2d+xvydExng0S8cWbe680QW\nL16MdDrdVc1r1P2nzOfz7q6uqv6MkpJOpyEIxXVGUkrRMxLByi5p5p8BIFHGDrrSp/5aQilFJppR\n3G7rjb++ge/c8R2IeREnnXQSrvrmVftkAH723z/Ds//zLFiOhdlixpWXFkRWqrGTllruU8yLCO4K\nwtZZXpqaEAJLuwXerV7wOh68oKx+CKDQwJTP5iHmxRkDaT5b6KBOh9PIxDNg1SwEowCjywhOfaBF\naDXXPJPkZ7WxbP8L9KOfoP+4r0FUTX8fGntfKKUHvEe/e6dQd77/nMMwv7FSWw1p2OtmWFWRhhkf\n2QghAiHkLULIB4SQTwghd+39+quEkK17v/4PQsi8vV//OSHkQ0LIPRPOcTsh5DOTnT+XyxntdmWM\n0ohiYVdS7B/YcDiFSCqHNkkaxPYG6BIaxCilELNi1cZtqkE2nq249lZr8vk87v/G/fjez76Hx370\nGF5//XXs7t+9zzHzF8/HT5//KZ7+89M4ecPJePp3T1dtJy31HHR4IAy9Uw9OKP95nuEYWNotCO4M\nKlbIRDAJSIcP7IyeaMfo+f/Ze/NoOe7yzvv7q7X3vfvuuto3YwuDFxDCZDKQyUwCIbzzQlje5GRC\nGBhCTmbemTfMkJCBSUiGJMQhbDEZIJw4BofVIdiAWWTLiyTLsq19l+5+b+971/p7/6jbrb7SXbq6\nu7qqBJ9zdCTd21X1u32r6/k9y/d5Ti8heykLtaHCl/RdH8c41N04xl5o6tPtqqRnlDrGD/8tqomd\nyO7qbHwyJ3LQpJWby6be+VfvHMNb71o7RG4HHMfxhBDLItGdnFgC8POU0n0AXg7gFwkhr1r+3juX\nv/73AP6cEHIHgDql9A4AdxNCwoSQEQD3Ukq/tdrJOY7jO+1pbTf1eh0+X+eh1zNz/SwQO4dGeBya\n2Lk37DbjDACNUgOesHO0m51w6vgpTGyeQCwYgyAI+MW3/CIOfu/gitfc9Zq74PEaP9fLXvkyLC0s\nWRbu7qcRaHYL8yV6TzkIfgFiSERl0TLZqKV4Ih7UC/VWHrk8X0b6XBpLZ5bQKDYgBkUkdiWQ2p1C\naDQEMSDavtHspgFIvxh+4SEI1cxyv+3OnvGsyK6QWjXzzpvjfvyxA/LON7LcUdKygpIN3zVq0PxE\n8ct/btwCPwFgOwAFgHd5R8ED0AB8FMAfrXZuQojP6/W6wzoDqFarphuUAOhLk5JuRkwqdaXn6UiD\nRiq5b6jH0sISUiMplOfKCG8KIzWSwtLC0pqv//ZD38b+f7Xf8px0r57qjd3C+kFoJIRatmZ76NUs\nqqRCqSmoF+pYPLmIymIFDM8Y4xhvs2YcYz/gfbwtrUqF8gKGXvoqstv/NarDnQ99atdCt+edP/Mu\nZ+SdbySVSgHAiFXn7+huIoSwhJAXACwB+AGl9PANL3kjgBOU0jMA0gCehzHlYzsAhlL6/BqnHkkm\nk92t3AbMGujT8yUMhzl4hd4+tFw9D7GyhKrJEZNKzQgXuwVd06FruiubkyhVBf6UHyy3/tq/+/Xv\n4syLZ/Dr7/t1ANYVjjEcA6p1b6DX6hbWK4QhCE+EUZwp9u2cVqCrRhvNFeMYdQohICC2LWb5OMZ+\nwXk5Wwz0+LOfAyUsZu95j6njOPF63ryZd/7or9zmmLzzjYyOjrKw0EB3tCWhlGoAXk4IiQD4JiGk\nuSV6kBBSB3AVwAeWX/t7zeMIIf8M4D8SQj4EYB8M4/75tlOPjoyMOGvLuQ61Wg1mNhSn50uYTPT+\nAfaluysQU+oKxLB7vFG5Yr8kpBvi8TgW5xbhTxqbt6X5JaSGUze97vATh/GFT34BD3z9AQji9cp6\nKyRYzWYl3bbarGaq63YL6wVPyIPKYgVSRXLMABeq01alerMD12rjGAkhhjLC5w5lRHOM4yAJzB1H\n9MoTmL3rN6EEzDlgnGh40E7OO7czNjYmABi16vymPr2U0gKAHwNoZvzfSSl9OaX0zZTS6fbXEkJ+\nBcAxAAEA2yilbwXw7wkh7cmskdHRUc4tRSONRqPjCu6qpGIqV+vbBCsKglrcXFc5taG6qmK2UWq4\nLrwNABPJCcwvzmNueg6KrOD73/4+7vuF+1a85uzJs/jYBz+GT3zxE4glbp5E1m9Pupd2n2pDRXWp\numG3sF4Ij4dRmilZdv6NoJRCqSnGPOvzy2008zUIfsFoo7knhfBYGGJQXFGNLwSEDZtpOAmGZQbb\nTUzXMPH0pyEFhrB4x1tNH87yLAoVZ+ed25mYmGBSqZRl3cQ29KAJIUkACqW0sFxS/gYA/xvAL69z\nDA/g9wD8Eoxepc07hAUgAKgBQDAY3MzzPH/w4MHmcRAEAaIorvlHEATYVVTWaDTg9XbmUZxdKIPS\n/syA9qfPoRHZBF0wV6hDqf0N/s2gVJWudLZ2okoqdEXH7//J7+MD7/gANF3Dm972JmzbtQ2f+/PP\nYc++PXjdL7wOn/xfn0S9WscH/+MHAQBDY0P4qy/91Ypz9dOT7rbdp9luYd3Ce3kwHDPQPvGarBke\nclGCUlfAeTl4Qh5EJiMdT3ziffYVXXVLs5J7EINLEmf/Bb7cZVx6/R+BcuZ/rzqAL56iqMkED73H\nmXnndsbHxxEIBMzlHk3QyU8/AuDvCSEsDI/7YUrpdwgh/3WdY94P4O8ppTVCyEsAfISQEwC+u+yF\nAwDi8fjuV77ylfi5n/s5AIaMSZZlSJK04k+pVFrx9abHzfP8hgadZdm+7cAURQHHdXbD9LfF5zmU\nx15h6hhd3Viv6SQopdBVfcMcrtOoLFYQGArgwJ4DOPD6Ayu+997/9t7Wvz/z1c90dL5+GWmW765Z\nSTfdwrolOBJEaa5kmYFutdEsSpAqRhtNT8iDwEgAvLc7yRMhxNCZy9ZNC+s3TT201b9TVipj7OgX\nUB7Zh8KW+zY+YBW+8VwVZ3PAn73FuXnndiYmJkAImbTq/BtaD0rpSwDuXOXrP7fOMfe3/ZsCePuq\nF+e4bWNjY63/MwwDj8fTURiZUgpVVVc15o1Go2XQVdUoOCCErGvIm975Wh/a5qagkw/1t47P4mPf\nPQMA+MOv5/DO/QHct6s7mQpfzUCoZU0XiKkNtSfd6qDRJA2s6I4HXhNd0yGVpL6HgvthpBmeMd1F\nSqpIaJQaSO4eTOGm4BdAddq3e7U5jlEqSWiUG4AOo43mKuMYe6EZ5vbGLO1R0TeaeV2rDfTIsb8H\nK1eWZVXm3+uTM0be+cC4gF+53R3FwxMTE5Blediq89v6BNc0bWhkpLsCOEIIeJ4Hz/Mdtd7Udf0m\nY16v11EsFlsGXZblFd75jZ64ruvI5/MrvnYj3zo+i//+jROoL4cXM2Udn/uh4U13Y6RbHcRMSqxU\nSXWVwZNr7mtJWs/V4Y15LcmR9Wqkzbb71DUdhWuFrruFdYs/4Uc1W+1qMEqzjaZUMjYWmqy1tNbx\nobhl0RjBK0BpKPDCHQaaFVnLZW2e3BWkTn0Lmd2/hHp8m+njCzUN9z9m5J3ff2/IVDrRTkKhECil\nli3UbgPtj8etnezThGEYeL3ejn7plFIoinKTZw4AU1NTra9pmtY6d9Nof+w7iy3j3ERSgQefrpg2\n0NELj2PTob8GBbDlh3+M2XvejfyO13d0rBWTd6xEqSkDCav2k1q2huiWqGXn78VIm2332Y9uYd3g\njXpRPl3ueFa2pmgtg6xUFXAeDmJIRGQiMrC1c14Otbx7+oqzAgupZOFsaEox8cxnoPE+zN79H0wf\n3q53/sfffgVQmIUkOWuW9VoQQsBYWBRlt4H2mpmtPCiaxWqCILRmP3u9XsiyjH379t30ek3TWkY7\nXZ1d9ZyZso7FU4sAjAcvy7PX/+aZFf9mORaxSz/E5JN/CVZdlnxUlzD55F8CQEdGWpM1V2mglZri\nqp7hmqyBgnZcXNQt3RppM+0++9ktzCyEIcbUpTUkdrqmG/KnYsPIIzPL4xiTAfCb7WkJ2wwZuwWW\nZy2bDw4A4WtPIzR7DFP7f8fUKNwm31zWO//ZW27HnpEQLtXSUBT3FOIt12dZgq0GmmEY1i1tPmVZ\nhiCs7uGxLAufzwefz4fRiBezhfpNr0kEGQzdlmoVQzXny+qKDk3VoDbU1r91Rcfeow+0jHPrOqqE\n0Wc/j9n4/hXGnLDkpgeV2zxoTdHA8O64FwCgnq/DFx2MQevGSHcqr2l2C0vsStgmZ/FEjRaaYlA0\noldVxai2bo5jDFgzjrFbCEMAuvpQBydipYEmmozxZz+LenQS6b1vMn38yRkJXz1cwZtfPoq33W3o\nnXmehyy7R8rGsixDCGGX+4X0FbsNtP2ftg5RFGVNA93Of/s3u1bkoAFA5IB37je8Q0IIWJ4Fy7Pr\ntuH0HlpdCyvWM1AbKuSy3DLmunbdU2p65lJZQj1fN6bptBlzhmMcJ70yU4DnFBqlBiITg5u4ZUUz\nE6u6hZmFEzgUcgVokrFR5f08PCEP/Nv8jt1kNkcjWh1B6QfNDYUVpE58HZ7SHM7/u48DjLn3oj3v\n/Ce/envr8y8IAmo196QQfD4fBeAH0Hdhv613l5VTQPqNLMvg+Y1Dxm++06hK/+DXX0JD1ZEIMl1V\nccuBJMTKzf2c5UASodHV5QdN71xTNEgVyZgMo2hQ62rLmGuq1vqwEpYYRpu/OeTe/DphbvbO+82g\nNJr9gurUlqpzs0aaEAKqr62Ft7Jb2HpoqpFHlkoS5IoxjpEwBP6U32gM4oKNWlO65AYD3aTfHj9X\ny2Lk+D+gMLkf5fG7TB17Y965Xe/sNg962UAHcCsZaEIIt337dud/EpdRFKXjPtxvvnMMx6fy+Nrz\n0/jb37y55WMnzN797hU5aADQOBGzd797zWPavXOGZeCLr70poJQahmY5zN407EpNWWHMW/2cCTY0\n5gzHdPUAUCV3Pejkmgzeb0/+04yRbhaKrfbeKg0F1aXqQCRVVKeQKst65LIEMIAn6DHaaE5GQAhB\naa4EXdVdYZyBZQ/awrxuvyHs8maN7d/7O3bk70A0FTOvep/pY2/MO7cjCIKrctCBQIAAsKSYys6n\not/M6Ea7MdOkBACGw15UJR11We9qWEazEGzLj/8UAIUcSGH27s6quDtpnUoIAWGNebHooHsp1a97\n582/NVmDXJMNY7789SYMy2xozJv5RDc1fQCWK85tlIR1aqRbzUpuqL2ilCJ/JW9ZtzBKKZS6Aqlo\nVFvrqpFHFsMigqPBVa8pBAQ08g34Yu54JrCcEeJ2C61uYn36ffuWziBx/ntY2PdrkMJjGx/Qxmp5\n53Y4jmv1r3ADy46bJRWudhrooJnJUHaj6/qquue1GFmeaZyt6BiPdfehyO94PSae/SwKmw9g6rX/\nuePj+r1TBow8FiuwHRlSSimoRlcYc13RIVdXGvNmEVMzxK3J2pqV7QzbnXduBXJVRiBpb8V5J0aa\n4RgjpXEDVnQLU6XremS1roL38RBDImJbYh3dM4JPQGnOvt7cZummEYydtKr6+9G0jeqYePpTULwx\nzN/5LlOHrpV3bodl2ZaE1Q0Eg0EGt6AH7SoDrWmaKQM9vGygc1UN47Fe3mYCUHMtG+1u80kIAeFI\nx3nlwnQBvJcH7+VbxlyVVOgV/Xple7t3vp5MbflvKwvhmgbIbjYy0qu1+5QqRpet5K7eQtu6arTR\nbJQaRh6ZZyGGRIRGQuC8nOnNVK/jMQdNL8NI7IBhmRXFpL0Qu/A4AktncOXnft/UfID18s7tuNBA\ns7gFDXRgOXbvCjRNMzWko92D7gVKCMyWYK5XGOREqG7oiTsJG3cqU2tVhjOkZbjXCrmvJlPbaA1O\neX/XM9IMz6zQ6/bSLazZRrNRbBvHGBThi/oQmYj05f0Y5FCHXul2GIldNHPQvcIodYwd+TyqyV3I\n7XiDqWPXyzuvuAbDuMpABwKBW9KDFgRBcMZTrgPMetBDoaaB7vFGIwTE5DhO1xlojXbs8XcqU2ui\nayuNua7q68rU2vPkqxlzEDjuvV3LSDMcA71tg2imW1izjWajaOiRNcVoo+kJexAcDlpiRFmRhSqr\nEDjnd5Qz0wjGCTQr+ntl+PiDEGpZXH7DRwATIpxTM/K6eed2nJLK6hSPx8MAsCSkZqeBZswYPLsx\na6A9PIuoj+/dQIMAt7iB1jW97znzJgxr5K87qRJvl6m1V7a3y9RUWYUma1g8tbi2TK3NqA9Cpgas\nbqTbG1R00i2sNY6xJEGpdTeOsRfM9g+3E7cZEcL0bqCF0hyGXvonZHe8AdWhvR0fV6xpuP97RUzG\nffjjNfLObobjOAJj0mP/z23FSTvETX1KutIQDoc9yJZ7KyTpKsTtoBBsJ1C9cw/aStq987WQKzKq\nmSoikxGjsl1ZWdnekUyt3aj3QabW5EYjHZmMtNa1Wrew1jjGkiF/ao1jHAqA9w1eRma2f/jP6Jx+\nGOjxZz8HyrCYvWdtqeeN6JTir79fRFWiePDdr0TARXLKTlm2Y7ecgWYZxkVWBOZ3zSNhL65kG71e\n1XSIGxQgcM9bSzX3bCh0XW/lrJsytY5CxhvJ1JaNepO1ZGrtXvpqm5p2I124VoCu6q1uYQzHtPTI\nzXGMQtAIW4fH+zeOsVs6bU/6M8xDCOlIfrkWwdnnEb16CLN3/xYUf+cFht94rooXp2T86QZ5Zzez\nbMduOQPtKg+6G4bDHjx3rdccNAPL+vT9DNN0u5noWaamri1TI4TcZMxDYyEUZ4rGeglBNV1FaaZk\njGMMi4in4s5ro0kAuCPC/dOFrmHi6U9BCo5g8fb/u+PDTs3I+OqzFfzKy0fxaxvknd0MwzCWdcW0\n00C7w2XqgZGQB6W6DlmlELgeftwedr6ugCyH5d1wSwxgiWZlaq2OcG35c1VRwQgMtLoGuS4jkojA\nl/CZUiLYQS9e3s+whuSZf4Y3fxWX3vAR0A4L+NrzzmvpnW8VCCGWTbSy89Oq3+ofxnYtdLd0k4N2\nG2768BJi3eCBbiEMacnUvBEvhKAAqSjBEzSakXgjXig1BekzaVQzVWeHkd1zK/xUwDaKGH3uiyiN\nvQKFzQc6OqY97/yZd96aeed2dF2HruuWtD6z1UDrupOfFL0zEjaGEPSkhSaM+Ry02yAu8pwIHGvg\nKKWoLFWQu5RDZJNRee2L+6BKKkKjISR2JqA2VCydXkJ5ody3xhX9QtfsbbDzM25m9LkvgZWrmH71\n+4EON9LNvPP/fNNtt2zeuR1N0ygsSs7Y+WnQdN1ZD4j16KbIouVB9yq1MnldwvRWEDJonOiVrgXL\nObODlKZoyF7MQqkrSO5OQvALaJQahm55KIjKYgUszyI8HkZyTxKgQPpMGsXZomMqp6lGHR+CdytU\nN69C8eQuI3nmn5He80Y0Yls6OuanJe/czrIdu+UMtO4mA91Nd5vhcD+alZgPcferKcHAcJEHzfCr\n97e2k3qhjsy5DPxJf2sARlMCxokcvDEvGsVGa2PBsAyCI0Gk9qbACRwy5zMoXCus6DpmB7qig+Hd\nYaDdcr82Md0bgVJMPP0paEIAc3f9ZkeH/DTlndtZjgRbYszsTA5obhopxrIszG4oAiKHgMj2FOKm\nXVRx90PzOEgYlgFVKeD8BlJGBymHNNPQNR3FacMDTuxKrKjKlipSaxgGIQT+pB/VdBXBkesdCQlj\nfN2X8KGRbyB3OQdO5BAcDtrSa1yVVddMNTPT/c4JmDXQkauHEJp7AVOv+V1ono3D1D8Neue1WJ68\nddp3LHUAACAASURBVMsZ6Fq9XneNFem2gftw2INspQfPhMB0Dtp1BnrZK+Wt6ZbXV5peQTeNa/qJ\nXJGRv5ZHIBWAL+G7aS1SSYIndH2OqC/hQ/pMGoGhwE0PakIIvDEvPFEPpLKEwkwBBATBkSDEYD/G\nH3WGm8aOWtn9zgrMGGiiyhh/9nOoR7cgveeNHR3TT72zrrtnLjgAVCoVDUDVinPbuQUsVyoV11iR\nbhu4j4S9PQ7MYMznoFniuAKg9XBTi0cAYAX7ZgFTSlGaLaEwYwy98Cf9qz7MpJK0wrgyLANv1Itq\ndu3nCCEEnpAHyZ1JhMZCqCxVsHR2CfV83fKQbmu4iUsezG7zoHVd79hAD534J4jleUzvfz/AbLxh\nOjXb37yz2bbKdlMulzUAZSvObecdVqlWLdl0WEK3HvRI2INcTyFu890b+jlabhC4rcUj7+Gh1Aef\nnlEbKtJn0wABkruSa3Yw01UdILhJR+0f8qO6VO3I2Ap+AfFtccQ2x9AoNbB0eslSiZbaUDvqyOYU\nNEVzTb4cQMdTwvhqGsPHH0R+8wGUx16x4euLy/Od+5l3dpuBrlQqOoCKFee21YOu1Wo2Xt4c3Ye4\nvchXNag9zLolJg8ljHuqooHV5xY7GSEgQK701mPdDJRSVNIVo7/2RASh0dC6D8JGqQExdHNomuVY\niEER9Xy942tzHg7RyajlEi25KkPwuaAIYRlVVgcyQKRf6FpnBnrs8OdBqIaZV71343NSik9+v4iK\npOPT7+hf3tmFBpriFvSgG41Gr32qB4cgCOimqG0k7AEFUKh1+UD7KWj1yXDOq4xej0Ea6JZ8qros\nnwpsbMRuzD+3ExgKoLJQMR2yXiHRQv8lWnJF7uhncwqa5J58ObDsQW8QkvcvnkL84uNYvP2tkEOj\nG57zm89V8cKUjP/5xpdh72j/9M6KokAQ3HMv3JIGmhq4xm3ieR6ybP6h3BepVZdvk1ukIJzI2S7x\nMQPLs9A13fJCvBXyqc3RjnOeckWG4F/9AceJHDgPB6ksdbUmhmUQHF4p0cpfy/f8+1tvzU5EkzRX\nedCgG8wxpzomnv4UZF8cC3e+Y8PTnZqV8ZVnK3jTvlG8/Z7+6p1lWQbPO79gtMlyqvaWC3G7Sgjd\nrQc9HGoa6C6NbJc92N0UNmY45vpYRpcgBkU0StZEgHTNmEBVTVeR2JWAN+Lt+FhVMqRK6z2MgyNB\nlOd72/A3JVqpvSl4Qh7kLueQu5SDXDO/iVUbKlhx/TU7DVdJwjrYqMfPfx/+9DnM3vse6Pz691sz\n77wp5sPH3tJ/vbPbPOhqtUpgkQdt6xZQ13XXxDW79aBHevWgSRfjJrFcaay4JwzHsEzHhSxOwBv1\nopatmTKenbCRfGoj1so/t8N7eRCGGHnfHr1WQgi8US88EQ/ksozSTAmAsQkQAkJH66/n6/CEVw/J\nOxWnzDDvhI0qzhm5irEjn0cltRe57f963XO1553/4bes0Tu7zYNetguW5LzsvsMUSeou1DZouvWg\nw14eHp7psZtYFwaat08K1A2ch7OlMrpbhIAAqSL1LczdqXxqI6Si1JGxCw737kW3QwiBGBKR2JlA\naNyQaKXPpjuSaNXzdXhj/d3oWImuuksDvdFGfeT4g+DreUNWtUHEzqq8cztu86AppTq1KJ9oq4Fm\nWbaWy+XsXELHCILQlQdNCMFwyNNbiLuL3z0juEy65OWhNtyTh25qhvsR5u5UPrURlFIoDaWj48Wg\nCE3VLHnPBd+yRGvrxhItpaEYM6w5d0R6AECuuaviXJPXloSJxRmkTnwNmZ3/BrXUnnXPY2XeuR23\nedBWRoJtNdAcxxXS6bSdS+gYURTRbdX5SNjbw8AMAtJFkRgnuKvwym0eNAD4E0b7zG6hlKKarnYs\nn9oIpaZA8HUWVgaWvegFS1JnAIyCtI0kWtWlKnwJn2VrsAKlptjSCrVbNFkDJ6y+aRt/9nOgLI/Z\ne9697jmszju302g04PW6I6KiKIployYB+0Pc0zMzMzYvoTMYhum6Knok7EGu2q3MqrvDOJGDJrnI\ng/bzXRUY2Qnv46GrelcboaZ8Sq7KHcunNqKT/HM7nrAHclW2PNKyqkRrpgiloUAqSfBG3fEwbqJU\nFVd50Gs1gQnOHEXk2tOYv/P/geqLr3l8e955EPOd6/U6PB531CTMzc2B5/m8Vee31UDXarXzc3Nz\ndi7BFIQQ0wMzgGY/bg16Vwa+Ox00K7Ku8qBZjgXVqKt6iAOGF1pZMKew6FY+tRHr6Z9XgxBi6KIX\nLVGI3MQKiZaHM8L6DFxVKwEYYXnO6x6JlSqt0lRFVzHx9GfQCI1i6fa3rHv8IPLO7WiaBo5zx/s7\nMzMDjuNmrTq/rQZ6YWHhzOzsrDu0QAA8Hg+6KWobCXug6UCxi2YllJCudNDtQx3cguATXOdFeyIe\nSBWpIy+0F/nURlCdQld101X7vpgPjUJjoK1hCUPgi/nAcAwCqQByl3PIXsq64neva8YgB7f0DAeW\npXfiyvsiderb8BauYeZV7wNl144GnB5Q3rmJm55XgGGgFUW5aNX5bTXQlNL5mZkZ538ql/F4PKjX\nO2+T2GSoFy00IaZbfTZxkxYaAITgYFto9gNCSEcV0XJFRvps2uhxvT2+YjRkP5DKUleTpwhD4Ev6\nUF0abF/8aroKX8wHf8KP5O4kAqkASjMlZM5nIJUkxz6o3dbxbLUhJFy9gJFjX0Jx/C4UJ/eveWyx\npuOvBpR3biLLsqsquGdmZrC0tHTaqvPbnYOen5+fd018y+v1dmWgR8KGp9Sd1Mr8sIwmbiu8EgJC\n1x2u7MQb80KuyqtWRFNKUZoroTDdm3xqI8zmn9vxJ/yoZWsDSy/oqo5quorAUADAskQreF2iVU1X\nO5ZoDZpGseEqzfZqIzxHn/siWKWOmVf/J2CNe1GnFJ/8weDyzk0ajYZr8s8AMDMzI9fr9Wmrzm+7\ngV5YWLB5CZ3j9/vRzYCP3tp9kq5kVoBRxOQmA90sbHPaQ3kjCCEIj4VRmC6s+HpLPgUgubt7+VQn\ndOtBA9dHUdaygxleU5orITAcWDX3LvgExLbFVkq00tZN0TKLVJZc5UErNQW893rFuTd7CYmz/4Kl\n296MRnTzmsd967kqXrgm4Y/eeNtA8s5NqtUq/H7/wK7XKzMzMyqAeavOb7eBTufzlhXA9R2/349u\nRmTG/QI4lnQV4qY9eFu8110GmhBibCpq7llzEzEkgmEZ1HK1vsunNkJTNBCG9FRs5h/yo7JkfoiG\nWeSaDLkmwxdfX1q1QqIlWzdFywyaooFhGdd0EAMApd4mCaMUE09/CpoQwPwrf2PNY07Pynjo2Qre\nuG8U77hn04BWalCtVhEIBAZ6zV5YXFykACyrdLY7B62rquqaUuNuDTTDEAyHxO600KQ7HTSw3Pyj\n7pq3F4BRdFUvmE8jOIHIpghKs0YetZ/yqY0wW729Gs1RlI28dRPmqE6Rv5pHdDLa8YaF5VmEx26W\naNnRhEcqSV2nEexCqV/3oCNXnkBw/kXM3v0foInBVV+/Iu/8qy8beDGc2zzo5T4et6wHDV3XXTN2\nkuf5rtp9AsZc6O66iRF0O26SMASUuku65Al70Ci64364kWbrT6pRRCYjA/O0esk/txMYCqC8WLbM\niy7OFOGL+VaEXDvlRolW5nwG+av5gXafaxQbPW+EBk1TA01UCePPfg612FZkdv/Sqq9tzzt/+p2v\nQNAz+GYsbjPQ9Xpdp5Ra9sCy3UALgrB07do1u5fRMQzDQNPM795Hwt6umpUYMivTh7VwWx6aYRkw\nHOMqDXe7fCq1NwXBLwxMW0wpNRpn9GFUIydy4MTuR1GuR71Qh9JQWoVh3UIYAn9ieYpW2IPclcFI\ntKhOjXyu3z0dxJo9wwkhGHrpYYiVRUzv/x2AWV1B0J53vm00PODVGiiK4po2n7quQ9M0S2882w00\ngHPnzp2zew0d4/f7UamYf/iOLDcrMe+ddB/iBgDBL0Cuuku65I140Si4w4teTT4Vngijnq8PpCK9\nqXHtVyiyH6Mob0SpKyjNlhDbGuvbOptTtFJ7Ui2JVvp8Go1Sw5IIQKPYgBgWXaV/bk4r4ytLGDn+\nj8hvuQ+V0Zev+lo7885NVFUFy7qnJ/uVK1fA8/ySldew3UDPz88fOn36tGtisKFQCOWy+QfYUMgD\nWaWoNEz+qKT7EDewbKBdpi32RDyo552dh15PPkUYgvi2OArXCpZHL6RSZ9OrOqV9FGU/0FQNucs5\nRLdELRuI0ZRoRcYjqGVqhkQr11+JVi1Xgy/mrp7hTQM9fvgBABQzr3rvqq+zO+/cpFwuIxhcPTfu\nRE6cOAEAL1l5DdsNdL1eP33mzBnXiF9DoRBKpZLp47qfC929zApwXyU3YAz6AIFjw9wt+RRdWz7F\nCiyiW6PIXc5ZWtDUKPYn/9xOv4Zo6JqO7IUsQmOhgfSu5n08YlsNiZZUlvom0dI1HWpDddWADMCI\n7sQq5xG79CMs7Hsb5ODwTa9xQt65SalUQig0OElXr5w+fVqfn59/yspr2G6gAZy/ePGia5qVdGug\nu9VC0x49aMIQMJy7Rk8Cy5OiMoPtbrURN8mnxtaXTwk+AeGJMDLnM5a8/5RSaJJ2c5/lHhGDIjSl\nt1GUVKfIXswikAr0taVpJ3Aih8hkZKVEa757iVaj2IAn4nFVeNu4NxRsfu6zkP1JLO77tVVf54S8\ncxO3GeizZ89KjUbDsi5igDMM9LSbBmZ02+6z5UGbLhQjID2G6sSg6LoOXd6oF428NfnEbuh2+pQn\n5LlupPs8FEKuyuD9vCWGoxcvWtd0ZM5n4Iv5NtQ7W8kKiRbpXqJVy7gvvK3UFEwWDsGfuYCZe98D\nnb95k+SEvHM7bjPQFy5c0AGct/IathvoZS10wy1yaEJIV3KrZEAEQ7oIcffoQQPLBrrkLgNNGGK0\n/nTAunudPuUJeRDZFEHmQqav6YZ+6J/XottRlJqiGe9Vyg9/0hlymV4kWmpDBdVpV9IwO1FzOey4\n/BAqQy9DftvP3/T9Yk3H/d+zP+/chFLquj7cCwsLFIBlk6wABxhoABBFcfr8eUs3In2lmzA3xzJI\nBkXkTGqhDZlVbwbajZXcAOBP2hvm7uf0KTEoIrY1htzlXN903v3SP69GN6Mo5aqMzPkMQuMhR3qc\nKyRaEQ9yV5clWut8NipLFQRS7uls1WT85D+Cl4qGrOoG46tTir/5QRHlhv155yZu68GtKApUVW1Q\n2oPEpgMcYaBVVX1+uSLOFUQiEXTTonQk7EW2bL5IrNcQdzMP7dSiq7XgfTw0SbNlXnBTPsX7+b5N\nn+K9PBI7EyjPl1GaK/UUvtc1HVSnfZ+K1Y6ZUZTVTBX5a3nEt8Ud38yDEAJvxIvU7mWJ1lwJ6XM3\nS7R0TTeiFFFn/zw3IuSmMDnzKDK7fhG15M6bvv+tY1Ucvybhw79sf965SaFQQCQSsXsZHXP27Fl4\nPJ4pq6/jCAM9PT391MmTJ11TxRSNRrsy0MMhT1c56F5D3MByh66SO7TFTZpeXHmxv7rc9bhRPhVI\nBvoa/mN5FoldCVCdInOh+7x0L8MxOoUwBL7E+qModVVH9lIWUklCcpe1A0GsQAyKSOxIIDKxLNE6\nk271U69la/DGvLaHf80y9vSnobMi5u7+rZu+d3pWxkPPVPDLd4zgnffan3duks/nEY1G7V5Gx7z0\n0kuQZfmY1ddxhIHWdf3c2bNnXRODDQQCXTUrGV5uVmKKPuSggWUD7ZLmH+14Y15IJQm6av2QBLWh\nIn1ufflUPyCEIDweRnAoiMz5DCpp80MqrMw/t+NPrj2Ksl6oI302DW/Ui9jWmKuGSNxIS6K1LQa5\nImPx1CLK82X4Es4L1a9HaOowYnNHMX3b26H6Yiu+18w7T8R8+NMBzXfuFLcZ6FOnTmnT09OWSqwA\nhxhoAOcuXLjgjHLdDiCEQBAEmO0hPhL2oC5T1KTOjU0/ctCAMRtakzRX9eUGjPfanzQmLVlFu3wq\nPB7eUD7VLzxhD5J7klBrKjLnMqYaygzCgwaMAitP1LNiFKXaUJG5kEE9W0diZ8KR+eZu4UQOkU0R\nBEeCYDgG2fPZniRag4RoCiae+QyqnmFk7/z3K763Iu/8DmfknZs0C8RE0T2DSE6fPi1TSi1vgekI\nA00pzWQyGUXXnf8haBKNRlEoFDZ+YRvXtdBmfs7ec9DA8qYiKLhObgUYmuh6rm7J5uJG+ZQYGOxD\ngmEZRCYjxiSsuRKyl7JQGutXemuyBoZjQJjBeECBVACVpQpUSUX+ah65yzkEh4OIbYuBFdzTmrFT\nKKWoLlYR3x5Hck8ShCGGRGvanilanZI89S14itM4u+M3QISV93F73vllY87IOzepVCquGjEJAGfO\nnNFhcRcxwCEGGgBEUbx6+rSlmu++Eo1GkcvlTB0zEjaqgHNVEx/yPoW4AUNb7PQWmqtBGAJvzItq\nur8V3b3Kp/oJ7zMKyPwJPwpXC61Nw2pYWb29Grqqg1KK9Ll0y+sfhPduF/V8HbyfBydyYFgGgaGA\nIdHy2jNFqxO4eh6jx76MbOqVqGx/zYrvOTXv3CSXy7kqvN1oNFCr1eqU0trGr+4NxxjoWq32g6ee\nsjyk3zfi8Tiy2aypY7pr99mfEDdgFMTIFdkxzT/MEBgKoJqu9iXUqGs68tf6I5/qN56wB8ndSQSG\nAyjPl7F0egmVpcqKHPwg8s+6pqOWrSF9zvAcg0NGyNdtHbXMQilFeb6M0MjKhhk3SrTyV/PrbqIG\nzejRL4BRGzg18Y4V97OT885NstksEomE3cvomKNHj0IUxVODuJZjDPTCwsKPjxw54poqJkEQoOs6\nzDRYSS17PWZC3JQw6JcHTQhx5fAMwAgF+1P+nictydVl+ZSvf/IpKxADIuLb44jviBsV3+czSJ9L\no7xYhlSRLOkLrckaqpkqshezSJ9NQ22oiE5GDc8+6Qfv4V2ZIjFDPVeHGBTXDN03JVrJ3UkEhtaW\naA0Sb+Y8Eme/i4W9b0bNP966p52cd25CKUWxWEQ47Kyw+3o888wzNJ1O/8sgruUkTcSx48ePOytu\ntAGxWAy5XA6pVKqj14sci7ifN13J3Y8cdBNvzNt6CLkNf9KPpdNL8Kf8xkANEzQ9o0axgdi2GHgH\nPqxWg+VZBIeDCA4HoUqqMfxBo1g6tQTOyxnjBL08OA8HVuhs7CSlFLqiQ5VUyDUZSk2BUlMMDznk\nQWg0BM7L3XSu4HAQxZmi43XO3UJ1ivJCGYmdnXlzYlCEGBSh1BVD2z5TQmA4AG90gNIsSjHx9Keh\nesK4NPl/wdvW0rOZd/7jN7/McXnnJrVaDV6vu6Rshw8frpdKpacHcS3HGGhK6eLExISm6zoYxjGO\n/bokEglks9mODTQADIe9yFZMeCF9zEEDxkOlMF0ApdRVHwrA8F5CYyFjtvCW2MYHLKM2VOSu5uAJ\nGuFjt/3cTTiRA8MxCI2H4E/4oTZUyFUZjVID6pK6QlPNcIzxcy7/qFSj0HW9dSuxPAtWZCH4BARS\ngdaYyfVoeu1yTR7IdKpBU14owxfzmY6q8F5DoqVKKiqLFZTny/An/fAn/JYX8kUv/wTBhRO49tr/\ngnKZQXy7YaDPzDk779zEbeFtADh58iQF8MIgruUYAw0AgiBMnTt37vY9e/bYvZSOiMfjuHTpkqlj\nRsIeXEx3XltACdO3HDRg5NI8QQ8axYajcq+d4gl7UFmsdGQkKKWoZWqoLFUQmYwMvELbCqSShMhk\nxOgJ7+VX7RFNKW3lrJuV7wxrVH33ajCCI0GU58uIb4v3dB6noUoq6vk6Uns632zfSFOipas6KksV\nLJ1egjfuRSAZAMP13+kgagPjz/4tavHtmN/0ejCzFbA8i1LdmO/s5Lxzk0wmg61bt9q9jI6RJAm1\nWq1BKR1ID2JHuar1ev0HTz75pN3L6JhmHtrM4AyjWYm5Qqd+hrgBwJfwrdC1uglCCMITYRSuFdbN\n+dktn7ICqlNo8sbjJQkhhofMs+BEruV598ObEwJCz6MonUhhqoDwRLgv7xHDMQiNhpDckwTDMEif\nTaMwXeh7y9rhF74CobqE6f2/g1pegi/uM/LO33d23rmJG/PPR44cgSiKJwd1PUcZ6Pn5+R8dPnzY\nVVUoiUQC6XS649ePhL0oN3RIaodGt49FYk14Hw+1oQ6kO5cVCD4BYlBcc5CDk+RT/USuyh2PubQK\nQgiCQ8GBtl+1mnqhbkSW+pxbb5doCT4BmYuGRGsjnXsnCOUFDL/4FeS2/SuUh29Ho9CAN+rFt49V\n8fw1CX/oQL3zjZTLZQQC/W2lazVPPfUUzWQy3x3U9Zz25Dr2wgsvuGprPjQ0hKWlpY5fP7z8EMh1\nXChGgD4PTCGEwBfzoZZzpxcNAKHREGrZ2gpPzsnyqX4waP3zWngiHsgV86MonYiu6SjNlhCZsG5Q\nA2EIfHEfUnsMidZGOvdOGDv8AACCmXvfA6kkQQgIOLug4B+fqeCX7hjBuxycd26yuLiIoaEhu5dh\niqNHjzaKxeIzg7qeoww0pXRhcXFRkWX3yICagzM6lViY1UJTizaXvoQP1UzVlZpowHjoRTZFkL9m\nvPdukU/1wqD6b29EN6MonUpxuohAKjCQjmgrJFrDbRKtojmJVmD+RcQu/wQLL/81KIEhVNNVaEEv\n/uqxIsajXvyZw/POTZaWlkwV2DqBF198UQdg+ZCMJo4y0ADg8XiOuSkPzTAMgsEgyuXOQn7m230y\nfc9BA2jlJ53SaKEbxKAIzsMhezGLwlQBsW2xvk+fcgq6alRgW1Fs1A1mRlE6lXqhDk3RbBmIIQaW\np2htiqCWWzlFa110DRNPfwpSIIWFfW+DpmhQZBV/e6iOUl3HZ975SkfnnZsoigJN01w1A3pubg6K\nomQH0UGsiTM+7W1MTU39w/e+9z1XhblTqRQWFxc7eu2w2W5ifZZZteNP+vvePnOQqA1DxytXZUQ2\nR1yjbe4GqSw5IrzdpDWK0qX3j6ZoKM2UEN0ctXVDx3t5xLbEENtuTNFqdo5bq+984tyj8GUvYfbe\n94JyHlTTVRxM83j+moQPv9H5eecm6XQayWTS7mWY4tFHH4Wqqo8M8pqOM9CKovz44MGDrioUGxoa\n6thA+wQOIQ9nrpLbojC0GBSh1BTX5RLbp09FJiJI7EwgfyXvam9uI5ySf27Hn/Sjlll9FKWToZQi\nfzWP0HjIMakQTjAkWsldSeiqjqXTSyjNlVqFnNELj+P2B9+GTU9+AjrDA7oCqlOcuFzD116SXZN3\nbrKwsOC6/PMPfvCD+tzc3DcHeU1H6aABgFJ6bXx8XFEUBTzvDo9IFEUQQlCv1+H1blyYZEitOqvk\n7GerzxshhMCfMrzo0Gho4wMcgKZoKFwrgOEYJHcnWxXa/oTfCHObaGDiJuSKbGkhUze0j6L0J/12\nL6djqktVsDzryCLCpkQrMBRALVND+mwamyqHMXnqs2A1w28huoKRH/8l7v9eEd+hBxDx8a7JOwOA\nrusoFouuGpABAM8995wO4NlBXtNxHjQAeDye59yUhwaAkZERzM/Pd/basBc5Ex60FTnoJv64daMc\n+01TPuVL+G6ST/mTflCdoppxZ8h1PVRJHeh4STM0R1G6pdhQKkuo5WqIbHLWZudGWhKt21LYfuHB\nlnFu4oWM/497GDoFqpKGH57pXEliN+l0GolEwjUbCsCe/DPgUAM9NTX14KOPPtq7WHCAmDPQHuSq\nHRpoCz1o4/TE8IIcLLnqRD5FCEF0cxSVxQqUuqtunQ2RShI8YWcW07A8CyEgoFFw/pwbVVJRuFZA\nfFvckZud1SCEQKxnVv3eKDGm6Umqjj//3rlBLqsn5ufnMTo6avcyTGFH/hlwqIFWFOVHBw8edFV5\nsdfrha7rkKSN0+fDYQ8KVQ2KtrHhpaT/OugbCaQMyYwTvaCWfMq7sXyKYRnEtsaQu5xzXV59PZyY\nf24nOBxEeaHsyPunia7pyF3KIbI5MhBJVT9R/Ku3VZ2j178+V3DHnHdd15HP5xGLuSsVtZx//sag\nr+tIA00pnZqbm3OVHhro3IseCXtAAeQ79KKJxc89lmchBkQ08s7xgiilKM2VrsunUp3Jp3gvj/BE\nGNmL2VuiaIxSCrWurtpz2ylwIgdO4Bw7xrRZFOZP+l3Z8rWa2HVTDK1GBXxcfWvr/6kgbwxDcTjZ\nbBaxWMxV4W3Anvwz4FADDQAej+fowYMH7V6GKcbGxjA7O7vh64bDRoi2o25iFoe4mwRGAo7xgtSG\nivS5NCilSO5KmpZPeUIe+JN+5C7nHPHz9IJSV1Yd/eg0AiOBnmd1W0VptgSWZ11VyNaEUeoILpxA\nLb4DRTEJnRLM6Al8UHk3HtEPAAA8PIN33RHCE088gStXrkDTnBs9mpmZwdjYmN3LMMX09DRUVc1Q\nSgcepnCsgb569erfP/LII65KJjYruGu19fO55rqJWR/iBgyZB+/j0Sja50W3y6fC42GEx7ofXuBP\n+CH4BBSnin1e5WCRis7oHrYRzclics1ZXnR5oQxN0hCecIc++Ebi5x4FJ5Xw7bHfxsuLf413jf4L\nvrz38zgWegMIgLGIF3/2ljvwgTe+Cvv374eiKHjiiSdw/vx5U0N8BoGmaSgUCojH3TUJ7Vvf+haV\nZfnrdlzbcTKrJpqmPfr444/LAJwb21uF8fFxzMzMYOfOnWu+ZijUeTcxOkDPKTgSRO5SDp6wZ+Ae\n21ryqV4IjgaRv5JHaa7kGhnZjTRKDUS3uEOO4rRRlNV0FVJZMorCHB6BWBVdw9CJr2E2uBsfOjKO\nAzsSeO/LGOzatgv/Y5UZyoIgYOfOndi2bRumpqZw6NAhpFIpbN26tSP5p9U0tc9u+1088sgjjfn5\n+X+w49qO9aAppblarbZ0+fJlu5diitHRUczNza0bWg15OHgFtmMPmgzAgwaMXCLv51HPDzaSmb8t\niAAAIABJREFU0yg2DPlU/Gb5VC8QQhDdEoVSV1CaL/XlnIOE6sZcZ05w7D56BUJAgCZrUCX7GwHW\ncjXUsjVXVWzfSPTKkxDLC/ho9t/iwI4E/vxN28HoKhKrGOd2WJbFli1b8LrXvQ7hcBhHjx7F8ePH\nO25HbBXT09OYmJiwdQ1mkSQJZ86ckQG8YMf1HWugAaBSqfyfhx9+2LkJlVXgeR6BQACFQmHN1xBC\nMBLydGagLWz1uRqhkRDK84PJRTflU5WliiGfivZ/l08IQWxrDErNfUZaqki2j5c0AyGkVdFtJ/VC\nHZXFCuI73GucQSm8hx/CFX0Y9a2/gM//+l24cvE89uzZ0/EpGIbB+Pg4Xvva12JsbAwvvfQSjhw5\nglwuZ+HCV0eSJCiKgmAwOPBr98Jjjz0GQRB+Qm0qZnG0gc7lcg8/8sgjzikt7pCJiQlMT0+v+5qR\nSGdaaENm1a+VbQwrsPCEPahlrNVFm5FP9YpbjbRb8s/t2D2KspatoTxfRmJHwtVzwC88cxgjlQv4\ncfyteOA37kWlmAfHcYhEzDdYIYQglUrhNa95DXbs2IGLFy/iqaeewuLi4sCKKN1YHAYA//RP/9S4\ncuXK5+y6vqPvYErphenp6Ual4q6xdqlUCtlsFqq6dqhvOOTtsB/34ELcTYLDQVQWK5bIlLqVT/XK\nCiM9V3JFdbdUliAG3SULao2iXBr8Z7ayVEE1U0ViZ8IxU7+64Uena0i9+DBKTBjv+O3fh8gxOHPm\njCnveS2i0Sjuuece3HHHHZibm8MTTzyBmZkZSyValFJMT09jfHzcsmtYAaUUhw4dUgH82K41OP4u\nJoR8+5vfHGh/8p4hhGB0dHRdydVI2INcRYO2YYvNwYa4AaMfsD/p73uoUpV6k0/1StNIa7JRkOZk\nI62pGkCcM17SDL6YD4384EZRNjd9jWLD9Z7zj07X8IMfnsbPsy/Ae+B98PgCmJ6eRiQSQSAQ6Nt1\ngsEg7rzzTtxzzz0oFos4ePCgZRKtXC6HUCgEQXBPugYAnn/+ebAse55SatvwJsffydPT01/82te+\n5o42OW1s2rQJU1NTa35/OOyBToFibYOHmE0Vj/6UH41ioy8FPy351MXe5VO9QghBZDICVmSRveDc\nZiZSyVnjJc0wyFGUlFIUp4tQJRXx7S7OOcMwzp95vIQ/iD4OynnB3/seqKqKS5cuYdeuXZZc0+v1\n4rbbbsOBAwdaEq1z586hn02irl69is2bN/ftfIPioYceUmZnZz9t5xocb6ABPPP888+rThbfr4bX\n64UoiigWV9fhdqqFpmTwIW7AMGThsTCK073piDVFQ+5SDnJVRnJ30hGdnAghCI2E4Iv7kDmXgSY7\n796SSu7LP7cziFGUuqojeyELwhDb5zr3StM4v3EL8NrGj0HufBfgj+P8+fPYvHmz5d4nz/PYuXMn\n7rvvPoiiiKeffhonT55Evd6bbyRJEiqViusmVwHAY489pkiS9M92rsHxBppSqgmCcPSHP/yh3Usx\nzebNm3H16tVVv9e5Fnr5oWNDONYT9oBSikapuzo9q+RT/cIX9yE8EUbmfAZy1VkNNuSKDMHvrpBg\nOwzLwBOxbgiLUleQPpeGL+FDeDx8SxjnAzsS+MTmZ0GoBrz6/ahUKkin05icnBzYWliWxebNm/G6\n170O0Wi0Z4nW9PQ0Nm3a5Lrfz9TUFMrlcoZSmrZzHc56Yq7BlStXPv3QQw/ZlgfolmQyiXw+v2pH\nn467ibVubHvypZFNERSni6Y8oZZ8atE6+VS/EIMi4tvjKFwrOGZsotpQwQqsq8O1AIxiMQuGsNQL\ndeQu5xDbEoMv5uvruQdNu3H+/Nt2gnv+S8CeN4FGN+Oll17C7bffDoYZ/GOaEIKxsbGWROvEiRM4\nfPiwKYkWpRQzMzOuKw4DgAcffFCr1+tfsnsdrjDQlNLv/vCHP3RdmJsQgk2bNq3qRcf8AniWONqD\nBozmJb64r2N50gr51A5r5VP9gvNwSO5OQqkpyF3O2Z6XbpQaEMP2pwJ6pd+jKCmlKM4WWxs/3ueq\nJoM3scI4//pd8Lz0ICCVgNf8LmZmZuD3+22f+tSUaO3fvx87d+7EpUuXOpZozc/PIx6Pg+fd93t6\n+OGHpXQ6/X/sXodbDHSDZdlDjz32mN1LMc2mTZtWlTEQQjAc9iBX3TgHbTeBoQCkorTunGW75FP9\nopnH9Ea8SJ9N29pT2u3553b6NYpSbahIn02DEILEzgRYzvkbv/W4yTgzOvDsZ4HJA5CTt+PixYvY\nu3ev3ctcQTQaxd133419+/Zhfn5+Q4nW5cuXsXXr1gGvsncuXryIfD6/SCldu8p3QLjCQAPA1atX\n//ff/d3fua6am+M4pFIpzM3N3fS9kXAnWmjDyNlRKNZawXLlc/5aftUHrd3yqX7ii/sQ2xpD/mp+\nYB3V2qGUQm2o4DzuaO+5EZzIgRXYrkdRUkpRSVeQvZRFZCKC0GjIVRu/1bjJOPMscPIbQGkG2P8B\nnDx5Ejt37nSs5xkIBPDyl78c9957b0uidfny5RV9H/L5PERRhN/vvgliDzzwgJLNZv/c7nUALjLQ\nAA4eOXJE7rWq0A62bt2Ky5cv3/Swb2qh14U0f0X25kYFvwAxIKKyeL0BBaUU1Ywz5FP9hPfySO1O\nQdf1gXvTSlUB7+Ndb4TaaQ7RMIumaMhezEKpKkjuTrqq7elarGqcKQWe/iSQ2IX54O3QNM0VXbc8\nHk9LoqVpGp588smWROvSpUvYtm2b3Uvsim9+85typVJ5yO51AC4y0JRSnWXZb3zlK1+xv4rHJF6v\nF36/H9lsdsXXh8NGP+71vDTaqhGz/8cOjYZQy9Wg1BVo6rJ8quwc+VQ/IYwhM4tORlG4VkBx1lyh\nXLc0Sg3X6p/XojmKUql1Nv6QUorKUgWZcxn4k35HKgC6YVXjDACXfgQsnoR67/tw9tx53HHHHfYu\n1CQ8z2PHjh0tidahQ4eQzWYdMUHLLM888wx0XT9HKV17mMIAcdVdPz09ff+XvvQl97nQAHbs2IEL\nFy6s+NpwyANFA0r19R789lZxt0MYguhkFNmLWaTPpA351JZb4+G5FryPb42/XDq71LXkrFOkkgRP\n+NbIP7fT6RANqSIhfSYNTdaQ3JOEN+K+h/xqrGmcAcN7DgzjRX0Xdu3aBVF05watKdGKx+MYGxvD\n0aNH8fzzz9s+RcsMn//856UrV678id3raOKqRBel9KWJiYlKOp32JZNJu5djilAoBIZhkM/nW6L9\ndqlV2LeGkSPNHDS13UTrmo5qpgpQYyiCk+VT/aQ5pckb9aI4U0RloYLwRBi8t785Ql3ToWu6Kyrf\nzSIEBRRnjY5fnHjzY0dTNBRnitBkDdEt0b6/t3ayrnGefxG4/BMU7vo9EN6D0dFR+xbaB+r1OgqF\nAu677z4AQCaTwYkTJ8CyLHbs2GF7Vfp6aJqGxx9/XKGUftfutTRxnesjSdIDX/jCF9ylt1pm165d\nOH/+fOv/w2HDwK2vhbZXZtWkXT6VelkKSk2BVHKdNL0nOJFDfFscwdEg8tfyyF/N93Vqk1yRXTcc\no1Oam5zKwsohGrqqozhbROZcBp6wB4mdiZ8e4wwAT38KlPfjpHg3br/9dnsW2UcuXryI7du3gxAC\nQgiSyST279+PXbt2mZJo2cF3vvMdsCz7BKXUMRMUXWeg0+n0A25sWgIAkUgElNLWrOjrHvTaFdrU\n5kYlq8mnGIZBdGsUhamCMdThpwwxICK5KwlPyIPM+Yzh+fXBUDeKt17+uR1PxAOpIkFTNOiajtJc\nCemzabACi9TeFHwx3y1VHLehcS5Mg578OmaHX4+X3X3AsVXbnSJJErLZ7KpRgEgk0pJoLSws4Ikn\nnsD09LSlU7TM8oUvfKF+9erVj9u9jnZcZ6AppdP5fH7+1KlTdi+lK3bu3NnyohMBESyDDbTQ9hlo\nVVKROZdZVT7FCRxCYyHkr6wuvbrVIYTAG/MitScFzsMhcz6D/LV8T8NFpIr7xkuagRACf8KP7CWj\nhoHhGKT2phBIBm6J6v92NjTOgKF7BqDe9dtdzXl2GhcvXsS2bdvW3WQFAgHs27cP9957L8rl8qoS\nLTsoFos4duyYBOBJWxdyA64z0ACwuLj4kfvvv99ZzZM7JBaLQdM0FAoFsAxBKiiuH+Juy0EPinb5\nVGg8tKZ8yhv1ghO5vo+ldBOEMYxOam8KnpAHucs5o7rdpDRLUzQwDHPLFtwpdQX5q3lU01WoDRWJ\nXQmjmc0tZpiBDo1zvQD92BeRHb4Pk3ccGPwi+0yj0UA6ne5YHubxeLB3795VJVp28MADD2iapn2B\nUhsbTqyCK58GkiQ9/Nhjj8mNhmNSBabYvXs3zpw5A2DjZiW0qYMekIE2K58Kj4chFSXLq5udDiEE\n3qgXyd1J+FN+lGZLWDqzhGq62lHrUDePl1wLSinq+TrS59IoThfhiXiQui2F4HAQtaw1QzTspiPj\nDEB66rNglBrC/+7Dt0RY//z589ixY4fpvuE3SrSefvppnDhxArXa4O4PSim+9KUvSQsLC58Y2EU7\nxJUGenmA9te+/OUvuzK2Go1GwXEcMpkMRsJe5DbsJgYMIsTdKDaQOZsxJZ8iDEFsWwzFqWJfZke7\nHUIIxKCIxI4E4tvi0FQN6bNp5K7kIJWlNdMBt4r+mVIKuSKjMFXA4qlFSGUJ0ckoEjsT8Ea8Rph7\nAKMo7aBT46zUK8CRv4W66bXgJ14x4FX2n2q1ikKh0FMFevsUrXg8jueeew7PP/88SqXOZgD0wqFD\nh1Cr1c5SSmctv5hJXGmgAWBmZuZjDzzwgGvdtqYXPRQSN2hWYn2Im+rUmObU5fQplmcRmYw4YtCE\nk2AFFqGREFJ7U/An/Khmqlg6tYTCVAGNUqP1O6eUQqkqrh0vSSmFUlNQnCli6dQSKksViCERQ3uH\nENkUualtqdWjKO2gU+NMKcX0dz8BUc6Du+8/D3iV1nD27Fns3r27L5EAQghGR0fx2te+FhMTEzh1\n6hQOHz58U5OnfnL//fc3rl69+j8su0APuEoH3Q6l9MLk5OTU8ePHd9155512L8c0wWAQwWAQ/pKC\nhkJRkyn84io3uMWtPuWqjPzVPPxJP8Kbup+rKwZF+BN+5K/kEdsWuyXCdv2i6VWLQRFUp5DKEuq5\nOopTRfBeHryfB8MzrnrPdFVHo9SAVJIgV2RwHg7emNfold1BXjkwFEDmvBGtcdPPvRqdGmcAOH3q\nJLZf+zowdDuw7ecHuEprKBaLkCQJ/e5L0ZRoJZNJFAoFXLx4EWfOnMH27dsxNDTUt3sml8vh2Wef\nrQP4QV9O2Gdc60EDwOzs7H//i7/4C1dKrgBDF62WMgDW1kJb1epzhXxqa3+mT/mTfrAii9Kc9WEp\nt0IYAk/Yg+jmKFK3pRAYDqBRbECVVCyeWjQKqTJGIZWTquM1WUO9UDe85NNLyFzIQKkr8MV9SO1N\nIb49bsikOiz6ao2iLLo2CAbAnHGempoCd/UnEEtXgf0faJv17l7OnDmDPXv2WLrJikQiuOuuu3Dn\nnXdicXGxrxKtT37yk6osy/c7rTisiWs9aADQNO2Rn/zkJ/V8Pi82u3O5Ca/Xi+2jceDFOWQrOjbF\nV3tV/2VWqqQifyUPISgguSvZ10ra8HgY2YtZVDNV+BPum2QzSAghEPwCCEOQ3Jk0pj5VZSOHO12A\nJmlgBRaclwPv4cF5OPBeHgxn3b5a13Rokga1oUKuyVBqCjRZA8MzEPwCBL+A4HCwL2sIDgWRu5pz\nbTtPM8Y5k8ng6tWreO3So0BoHHjZWwa4UmtYXFwEz/MY1LPX7/dj3759aDQauHz5Mg4ePIhNmzZh\ncnISHGfelOm6ji9/+ctSJpP5lAXL7QuuNtCUUm1oaOhTf/M3f/PBD3/4w678We7aux14dA6ZogJg\nlSKh5RB3P8ZNUkpRy9ZQWawgMhmxZMAFIQSxrTFkzmfA8uwt2Ve6n1BKDUMssitC4UEEje8pGtS6\nCqWhoJatQW2o0FUdYACWY8HwDFieBcMZfxPW6OAEghV/U0pBNeOPruvG35oOXdWhSio02YjgEIaA\nEzlwIgchIBiNaSwKv3MeDizPQiq7T/9txjiXSiWcOHECr54UQX70FPALfwKw7m5Kous6zpw5g3vv\nvXfg125KtHbs2IFr167hySefxMjICLZs2WKqj/nXv/516Lr+Y0ppzsLl9oQrjVo7S0tL93/5y1/+\nL3/wB3/AmS3xdwIjUT8IgLn5OnBHYO0X9uhAa6qGwtUCCEtawx+sgmEZxLfHkTmfAcMxri1+GgRy\nRTa86FUMICEEnMCBE7ibNjpUp9BUDbqiG525VB2abHToAsWKArTmvUNYAoZlWn9zPAeGY4yZzTxr\niyY5OBJEabbkKgNtxjjXajUcO3YMd911FzyP/g4ghoBX/PoAV2sNV65cwcjIiK0Tq3iex/bt27Fl\nyxbMzMzgmWeeQTwex7Zt2+Dz+TY8/v77769fu3btQwNYate43kBTSrOTk5M/+epXv/pv3/72t7su\nqSNwDOIBAdmKEU7kfSt31rQPRWKNYgPF6SJCY6GBDbhgeRbxbXFkL2YR3x6/qZL3Zxh0q38mjGG8\n4fK9j+ATWlXgN977TsSMcZZlGUePHsW+ffsQVDLAmUeM3LMnNMAV9x9JkjA1NdUaiGE3LMticnIS\nmzZtwvz8PI4dOwafz4cdO3YgFFr9vT5+/Dimp6fnKKUvDXi5pnCfy7kKU1NT/+/HP/5x11abjIS9\nqBAehenCKoVBTZmV+RB3r/KpXuE8HKJbosheyv5MI70GjfKtoX/uhU5HUdqNGeOsKAoOHz6MXbt2\nGROcnvk0QFjg3vcNcMXWcPbsWezcuRMs66ypa02J1oEDB7Bp0yacOnUKzz777KoSrY985CON2dnZ\nD9iwTFPcEgaaUno2n8+/9P3vf9/upXTFcNiDfJ2CEznUczeMu+4yJiBXZSydWQLn5RDfEbdthKHg\nFxCZjCB7MdvKc/4MA13TAR235HhJM4hBEaqkOnoTZ8Y4q6qKw4cPY9u2bRgeHgaqWeD4PwB3vBUI\njQxw1f0nl8uhWq06eixmU6L16le/Grt378aVK1dw6NAhzM/Pg1KKy5cv49ixY3ld1x+ze60bcUsY\naAC4du3a+z/60Y/WN36l8xgJe5Cr6AiPh1GeLxtFQMuYbfVJKUVpvr/yqV4RAyIiExFkLmT6Op7R\n7UhlCULQ5THqPkAIQXAoiMpiZeMX24AZ46xpGo4cOYLNmzdfN2JH/w5Q60Z428Xouo4TJ07gjjvu\nsP2Z0intEq2lpSUcPHgQH/rQh5R0Ov1fqZN0jGtwyxhoSumxmZmZa0ePHrV7KaYZDntQkXTI1Cia\nKc4U277b/CBsHOJuTZ/SlqdPOWiurhgSER4P/8xItyEVJXhCP6tyBwBP1AOpLDlufKlZz/nIkSMY\nGxvD+Pi48UWlDhx5ANjxC0Bqz4BWbQ2XLl3C8PAwAoF1ilkdSlOitW3bNhw6dKgmSdJX7V5TJ9wy\nBhoArl279p/+8A//0HVedHMudK6iwxvzQpM1SOXl/iutaVZrH79i+tRYCOHx1adP2Y0n7EF4LIzM\n+czPwt249cdLmoEQgkAqgOpi1e6ltOgm5zw2NobJycnr33jxIaCWAfb/7gBWbB21Wg2zs7PYvn27\n3UvpiT/7/9k77/C2yrP/fx9Jlq3hvS1bHomdYTuJM5zpkAGkoYw0jAYCSWlYbaGUwK+FlveFQiGQ\nl5myEtow27LJINBMIDtxEtuJV7xtWR6yZUnWHuc8vz8UmwwnXpKPJJ/PdfmyrfGcryXrfM9zP/dz\n3y+8YDcajU9SSi84ARFCGEJIMSGkhBByihAy59ztaYSQ0ovHIYS8Twi55dzPUYSQIkLI3Z7WG1AG\nDeCH8vLyjvLycq51DIqEMHfyltbEgBCCCGUE9E36c80EzhntZZLEerpP2Y12d/cpHz/hh4SHIELp\nDnf78pqjN4ms3oOcf63A0kN3IPeT2xFZvYdrST6BNFoKq87qE/Xch2LOaWlpUCqVP93BMsDhN4Ck\nPCDNf1tKUkpx+vRp5OTk+Fxi2GDQ6/XYsmWL3WAwvNvH3VZK6RRK6WQATwBYN5AxCSHhAHYC2EQp\nfc+DcgEEmEFTSmlLS8vDTz31lF+V/+yZQfe0nRSFiCCNkqK7pRuUXL6S2Pndp6LSo/yml3BwaHBv\n4pjT5uRazogSWb0HqQdeRrBZAwKKYJMGqQde5k0a7q1j0mgpzJ3czqIHY852ux1HjhxBRkbGpb2Q\nz34LdNX6fVlPlUqFkJAQxMTEcC1lWLz44otOh8Px8rluiFciDIBuAEPKAXwH4N+U0reHLbAPAm5z\nKsMw244cOdLd0NAQm5aWxrWcAZHQa9A/RV3kCXJ0nO2Ai7k0FExZCoPK3d4xZlyMX2YBB8uDEZUe\nha6aLkSmRUIs5zBZilIQ1gXCOCBgHO7vrvN+7v3uvMx9zotuc170PPdzCOOApKsWAvbC91ToskNR\n+A/oMq/m6AXwHWRxMnRUdEAeK+dkmWYw5mw2m1FYWIiJEyciLi7u0gcc2gBEKIEJN3lRsXexWq2o\nq6vD3LlzuZYyLCwWCz7++GN7Z2fnq5d5iIQQUgwgBEAigIF0MnkFwD8opZcbc9gEnEFTSlmZTPan\nZ5555p3Nmzf7RYpsSJAQEdKgCwyaEIKxbBFSj70FAMj65lE0z7wf7UnzPdJ9yhcIkgYhOjMa2uoO\nRCSEQCoXXt7kBmyYV7itD8MUnPsaLpQIwArFoEIxWJEYVBAEViT+6TahGFQsA2H7XnsXmzqGrSEQ\nOL8V5UjXch+MOev1ehQVFSEvLw8RERGXPqDpKNB8HFi6HhD652mWUori4mJkZ2cjKMh3Ek6Hwuuv\nv84wDLOZUnq5DfdWSukUACCEzAbwISEkp59h9wG4iRDyEqVU40m9Pfjnf04/WCyWj3ft2vV/jY2N\n0RckbPgwCWEh0Jp+MorI6j1IPfoahC53NEZs0SJ1/8voHtsNkn+D5zK0e2ePzksNsK+Z4pUM0+UA\nYS8/m/zJFC8yTHb4a9GsIKjXHFlh0E+meO42RiyFSxDuNk/hhcZ54W1BV7jvwrHPvw+CgUUxcv69\nAsGmSz/LVCBAmOo4ulPyh/1a+DvyeDk6q0e2FeVgzFmj0aC8vBz5+fmQyS5zEXFoAyCJBPLu9JJi\n79PY2AiZTObxVpIjjcViwTvvvGNrbW19ZiCPp5QeIYTEAOjvD/8EwCEA3xJCFl7B/IdMQBo0pdQp\nk8ke+uMf/7j5008/9Yt9LInhIWjs+qkYmqLwH73m3IOQsSO74UO0xIoGEHZ13+c2zR5zdPY5iyTD\nLPR9wexReL5R/mR4rqDwK5oiQ4JgNrgAcTCCY8JBRcEXzj7PjdOXwVKh6Ly+2b6NesY9SN3/MoTM\nT+8tKwiCSyxD5nePw5CSj+aZ98MWlc6hSm4RBgkhlrpbUY5Ep6vBmHNdXR1aWlowe/bsyzdm6Kx2\nrz/PfwwQ+2dHN4vFgoaGBsyb57/JbT08//zzTofD8Qal9NKSYn1ACBkPQAhAC+CKRb0ppa8SQhIA\nfEUI+TmldPjhuPMISIMGAIvF8unhw4f/r7S0VJGT01+kgnsSwiU41fTT/8/lQp5B9m6kHnztgtsu\nP3vsMTUJXMEXzx77myle2RR/ui3IXcLQAzOdnh7VTosTUWn+k/Q2GHSZV8PYZsT4pk8hNnfAIY+F\nesY90GfMR2zZFiSe+ggTv7wXHROuR+u0X8El6SN8OgoITQiFrkHndYMeqDn3FOlgGAazZ8++cjbz\nkTcAoRjIv89Lqr0Ly7I4deoUJk2aNKQ2jr6EVqvF+++/b21ra3u2n4f2rEED7q0zqymlzLkIzjhC\nSPN5j33k/CdSSv9ECHkPwEeEkNs92Vvav1/9K0ApZYVC4a/Wrl27fdeuXT4/i04MD4HBysLpoggS\nEThksQg2XxoKdUijUbH8nQtM1F9mj/1BCEG4IhwWrQUdZzsQnRF4TTYoS6GOmgNXwS8uuU8z6TZo\ns5Yg6cT7iK3YjqiavWjLuxOanF+ACv0incJjiEJEEAQJvNqKcqDmbLfbcfLkScTGxmLs2LFXDrub\nNEDxf4AptwPyPhLH/ICqqirExsa6a4j7OU8++aTDarU+RSm94tYASmmfbz6ltAFAX+uJn1/0OI/v\ngQYCbJvVxTAMs6eysrLh4MGDXEvplxaDu77Kirfacf8/2/GN9BYwwgtPTIxAjNqJv4JLGg0mOBRU\nFBww5nw+0mgpIlPdTTaser+rO3NFHGbHFdtvMiHhUM17GOW3/APm+BwkH9uI7M/uRkTd/gGXew0U\nvNlEY6DmrNPpcOTIEYwZMwaZmZn9r4kf3wQwDmD2g15Q7X06Ozuh1WqRlZXFtZRh09TUhG3bthm7\nurre5FrLUAm8s/tFqFSqux577DGfPstvKVLjq5Pq3t87zRRPtM7DzswHYZfHgYLALo9DQ8GjqA+e\nDqc18PcOi2VixGTFwKQxwaA29NHlyz+xGWwDKu9pi0xDzdJ1qLruRbCiYIzZ8zSyvnkE0o6qEVDp\nG4hlYlCWevz/faDm3NDQgDNnzmDGjBmIj4/vf2CH2V13e9x1QEymRzWPBA6HA2fOnMHUqVP9endI\nD3/84x9ter3+95RSvz1hkkA58V2J1NTUQ3//+9/n3HjjjVxL6ZO5L+yDuo+ZYkyoABvvvjBM5jA7\noG/UI3Z8rE+W8/Q0lFJ0q7vhMDsQlRHll3u+z0dToUFMZgwEokFcG7MMYip3IOnE+wiy6aHNvBbq\n/DVwyvw7u3Yg2LptsHRaEJXhmXDrQMzZ6XTizJkzAIDJkycPvHrWsY3Ad38Efr0TUM7yiN6RglKK\n48ePQ6lUIjHRvztuAUBZWRmWLFmiVqvVSk+uCY80AT+DBoCmpqa7n3jiCQvL+t77RClJI4KZAAAg\nAElEQVRFy2XCuJ3GS/WKZWJIo6XQN+m9Lc0nIIQgPDkcoQmh6Dzb6dch754uZYMyZwAQCNE58UaU\nrvgQbZNXILL2e2R/uhqJJz+AwOm/r8dA8GQryoGYs06nw6FDhxAbG4upU6cO3JwZlzs5LDnf78wZ\nAGpqaiCVSgPCnAHgkUcesba2tt7tz+YMjBKDppRWGY3GXZs3b/apN8tiseDw4cOIlV0+Eerd77th\nsFxY3EIWJwNlKcwdvtNYwNuEhIcgZlwMzBrzeXXK/YvhJjyxYjnUM+9D2W3voTslH0knP0D2Z6sR\nVbXrsrXa/R1CCOTx8mG3ouzPnCmlqKmpQWlpKaZPn46UlJTBHaBiK6BvAub6X1OMjo4OaDQaZGdn\ncy3FI+zfvx+VlZWNDMPs5lrLcBkVIW4AIIQkpqen15SWlkql0itubfM6lFI0NzejtrYWubm5ONBk\nwxNfnYH1vDaMIUECzEiNxOE6LcQiguXTZPh5ngzBIndYm2VYdJztQGRq5BWTjgINSilMGhMsWovf\n/e26Rh2kkVIEh3kmK1nWdgYpR96CrOMszLHj0DzrNzAlTvLI2L4EpRSaco27rK1o8Esc/Zmz2WxG\ncXExIiIiMGHCBAgEg5y3UApsWgDYjcCDhQMuWuMLWK1WHD16FLNnz0ZIiM9vdukXlmWRn59vO3ny\nZAGl9ATXeobLqJhBAwCltNVqtb7+5JNPcpow4HA4cOLECXR2dmLu3LmIjo7GsjwF1i3PhSJCAgJA\nESHBC8sn4aN7ZmHXI1dh3tg4/OuICb//qBM/VFjBUgqBUIDojGjoGnQ+10PXmxBCEBofiqj0KOib\n9O4EMj+ZTTtMDo/WHDcn5KJy2ZuoX/A4gixajNv+B2Tsfhri7haPHcMXGE4ryiuZM6UU9fX1KCws\nxIQJE5CdnT14cwaAhoNAazEw50G/MmeGYXDixAlMmjQpIMwZADZt2sRqNJo9gWDOwCiaQQMAIUSs\nUCjU33//fUxm5shnWWo0GpSVlWHcuHFISkoa1HOP1Wnxtx0VOKM2ICM2CKvmyZGbEgyr3gqTxoSY\nzJiAyLwcDJRSmNpMsOqsiFBGcNtwox9cdhf0jXrEZHmnIxBx2ZBQ8hniSz4BYRlocpajdepKsGK5\nV4430lDWPYuOnRA74AI2VzJnk8mE06dPIywsDBMmTBheG8V/3QqoTwGPlAJB3q985gl6WkjK5XKM\nGTOGazkewWAwYNKkSaampqZ0Smkn13o8wagyaAAICgpaUlBQsGXfvn0jdsnIMAzKy8thNpsxZcqU\nIV+tsizF9tMtePG/lWjR2zA9PRh3zQ1FmM0C1sUiQjk6q045rU7om/QICglCWHKYT1YgM3eYwTIs\nQhNCvXqcIHMHkgo3I6ZqJ5whEWiZ/it0jv+5X83sLoex1QgicK9J98flzJllWVRXV6OtrQ25ubnD\nL8ahqQDemgUs+DOw4E/DG2sEqaurg16vR15eXsBc2K9Zs8a+devWJ67QscrvGHUGDQBKpfLIq6++\nOvPmm2/2+n+mwWBAcXExlEol0tLSPPJhsDkZvH+4AW/sq4bFweDqbAmWJLgQExMMeVxgzJgGC6UU\nFq0FpjYTQpNCIYmU+NSJR1urRVhiGIKkI9MVSNpRheSjbyG09TSskWlonvUbdKfMGJFjewuWYdFR\n0YG4iXFX3GJ4OXPu7OxEWVkZkpKSMGbMmKGFsy9my2+B0q+AR8oAWfTwxxsB2tvbUVNTg1mzZg0v\ncuBDFBcX4/rrr29Wq9VplNKAWfMblQZNCFFmZGRUlJWVSb219tKTFdra2oq8vDyEhnp+5tRldmDD\n3mp8fLQRQULgmlRg2awwhEf5R5jNGzBOBoZmAxgHg4iUiBEzxCtBKYWmTIO47LiRvWigFBENB5B8\ndCOCja3uRhyzfgNbpH90eOsLg8qAIGkQpNF9J3r2Zc4WiwXl5eVgGAY5OTmX70A1WLpbgNcmAdN+\nBfz8Jc+M6WW6u7tx6tSpKzf78DMopcjPz7eeOHFiEaX0KNd6PInvxQJHAEppk8Vi2fDkk096tPNI\nDz3bp1wuF+bNm+cVcwaAKJkYT9+YjV2PzMf8rDhsq6F49HMD9paYwI7CCy/A3QkpKj0K4cnh0Dfp\noWvUgXFye0HttDgRJAka+Rk9IdCnz0fZbe+heeb9kLeVYeIXa5By8HUIbYaR1eIh5AlyGNuNfVaW\nu9icRYSisrIShYWFUCqVmDlzpufMGQCOvQNQBpj9O8+N6UXsdjtOnTqFqVOnBow5A8A//vEPpr29\nfV+gmTMwSmfQAEAICVYoFM379u2L8VTd2Yu3T0VHj2zI63h9F/667QzKWk1IixFhdUEoJqUEzgdx\nsFBKYe2ywthqhCRKAnm8nJP1aWOrEYIgAWQx3LYeFFn1SDz5AWIrtoMJkqB16l3oyF7md404dA06\nhESEXNDp6nxz3njnVGha1aivr0dqairS0tI8E84+H1s38Go2MGYRcNsHnh3bC7hcLhw9ehSZmZkD\nK1vqJ+j1ekyaNMmkUqkyKKV9twD0Y0atQQPuhLF58+Zt3bdvX/BwZzcOhwMlJSUQiUTIyclBUBA3\noVWWpfjPobN4dV8dOq0UU9OCsWpuKFKiA6sr1GCg7Lm9050WyOJkkMXIRrRMasfZDkSlR0Eo9o31\nvhBdA5KPvoNw1XHYwpKgnnkf9GkFHmkZOhK4bC7897AW2xsJtEYWshACk42iIDMGf706CU31tYiP\nj8fYsWO99zk8/Aaw6y/AvfsAxTTvHMNDsCyLwsJCJCQkIDXVf5c3+mLNmjWOc4lhr3CtxRuMaoMG\nAKVSeWjdunWzVq5cOeRL7OFsn/IWdY0q/HN/NbbVOmG2u3B1tgS/nCVHhNQ3TIILWIbt3ZYli5dB\nFu19o6YshaZCg/hs35u1hKkKkXz0LUh0jTAmToZq9m9gjfH9Lkb7z1rw9u5uOM4rniYgwK9zgnFj\nbhyysrK8u6+XcQKvTwEi04C7d3jvOB6AUoqSkhJIpdKA6FB1PkePHsUtt9wScIlh5zPqDZoQkqhU\nKqtOnjwpj4kZ3B7Vnu1TJpMJeXl5PrfZv7a2Fqr2LvzQKe1NJFs2TYYb8mQIDvKP2ZI3YF0sjO1G\n2HQ2yGJlkMV6z6ht3TbYdDZEpProFrjeRhzvQWTrhjbrWrTMWAOnzDv7tT3B/e9p+qxTnxQejMNP\nXO19ASWfAl/fB9zxGZC1xPvHGwaVlZVwOBzIzc31qV0Nw8XhcCAvL89aXl4+h1JazLUebzEqk8TO\nh1LaajAYHl6zZo1tMM8zGAw4ePAgZDIZZs2a5XPmDABjxoxBbLgUt2RQ7HpkPhaMi8d/jprw4Ied\n2FduAeMnFbg8jUAkQLgiHLHjY8EyLDTlGnS3dHulIpvdYEdwuA/nAfQ24vgI7ZNuQ1TNPmR/ugqJ\nJz8EcQ3qIzEiMCzt05wBoNVg974ASoHDG4CYccDYa7x/vGFQX18Po9EYcOYMAE888YRDp9NtCmRz\nBvgZNACAEEJSUlKOr1+/fuqKFSuueNEyEtunPElPiCskJATjx4/HiYYu/G1HOYpVBqTFuCuSTVb6\nsIGMAJSlMHeaYe4wQywTQx4vR5DEM2uXPTWkfbF4Sl+Iu9VIPvYuIuv3wyGLhTr/HnSNXQwQbvVb\n7Cx2nzZjR4kFWkvf5yxFhASHHl/kXSE1e4GPlwM3vgFMvcu7xxoGTU1NUKvVyM/PD5i9zj0cP34c\ny5cvb1Gr1emUUq/sxPEVeIM+ByEkMSUlperUqVOXDXVbLBYUFRUhMjIS48eP93xmqJeglOLUqVMI\nDQ1FVlYWKKX45nQrXvxvJZp1VkxNDcZd8+RQRnO/Z5hLKKWwGWy9nZNksTJIIiRDDn8zTgbaGi3i\nJsT1/2AfQ956GslH3oKsswrm2HFQzf4dzAk5I66j0+jCtuNG7Dtrh9UFTE6SYXp6DP59XAWr86eZ\ntCRIiHXLc7EsT+FdQR/eBGgqgT+cBkS+eWHb3NyMxsZGzJw5EyJRYCWHOp1OTJ482VpRURHQoe0e\neIM+j4iIiF/Nnz//7W3btl0Qr+Z6+5QnYFkWJ0+eRFRUVG/tXbuLwYeHG/H3fdUw2V1YNFGCFbPk\niJQF1hX3UHDanDB3mGE32BESEQJZjAyikMGd7CxdFrisLoQpwryk0stQFlHVe6A4/i7EFi26Mq6C\nOv8+OMK83zO4psWGLceNOKZiQClw7YQY/HbROExOca/lbylS4/92nkWL3ooYmRB/uX4EzLm1BNg4\nH1j8FFCw1rvHGiItLS2oq6vDrFmzAs6cAeCRRx5xfPrpp2+2tLT45hvgYXiDPo9zoe6jL7zwwvQ7\n7rhDAPy0fUooFCI3N5ez7VOeoGe7RVxcHNLT03tv15kd+Pu+Gnx4tAEiAXDTVBlunCpFSJB/RAi8\nCWUprDorzFozKEMhjZZCGiWFQNT/a6Nr0EEaLR1WD2hfQOC0Iv70Z4gv+dTdiCP3ZrTmrQQr9uy+\nbsbF4Hi5CdtP23C2i0ISRPDL6SlYUzAGKVF9Vw6jlOLHH3/EnDlzIBZ7eT/3l/cAZ79zl/WU+F7S\nX3t7O6qqqjBr1iy/Pk9djnNZ2+pzoW1OuxKOFLxBXwQhJEGpVFafOHFCTin1ue1Tw4VhGBQWFiI+\nPv4CkwaAhk4z1u+sxLdn2hAlE2LFLBkWTJBAOIJ7hn0ZxsHA0mWBpcsCYZAQkkgJJBGSPs26t7xn\nP3Wj/YkgcwcUx/+J6Opd5xpx3I3O8dcNqxEHy7AwddnwfZkZu2sZtJqBWLkYawoycHu+EuEDyAVo\naGiA3W7HuHHjhqyjX/Qq4PXJwMwHgJ89773jDJG2trZec/b6hQoHOBwOTJkyxVpRUTGLUnqaaz0j\nBW/QfRAWFrZqzpw5/3zqqadEvrh9arj09IGNjo7G2LFjL7n/ZGMX/vZNBYpUeqTFiLBqXuioTyS7\nGKfVCavOCpveBiIkkERJIAmX9BYjcdlc0Kv0iMn03e1KQ0XacRbJR95CaNsZWCPToZr9AIzJA2/E\nwbgY2A12dLRb8H2dC/vVBAY7xYSEUNw7PwPXT0qCeAARit7xGAb79+9HQUGB98K6//2zu7TnwyVA\nRIp3jjFEWlpaUFtbi5kzZwakOQPAww8/7Pjiiy/eUKvVj3KtZSThDboPCCEkNTX16JNPPjntnnvu\nCcgF2Z416bCwsD5nHpRSfHumDS98VwGVzoq81GCs4hPJ+sRlc8Gqt8JmsIFlWISEhoBSCqFY6PX2\nkpxBKSLqDyD5WE8jjplonvVAn404KKVwWpywGWywGWzosFD80CbEwUYGDhfFVVmxuLcgA3PHRg95\nO1B1dTWEQiEyMjKG+5ddilXvLus57jrg5nc9P/4w6EkIy8/PD8iwNgD88MMPuPPOO1VqtXrMaAlt\n98Ab9GUghEQpFIraPXv2RIwfP55rOV6BZVkUFxdDIpFg/PjxfZ4c7S4GHx1xJ5IZbXwiWX+wDAt7\ntx36Jj2IgEAoFiI4NBjBocEQy8QBE+7ugTAOxJV+jcRTH0PgsqJj4o1omboKVlYKu9EOu9EOxs4g\nSBqEJocIO2tcKKxzQCgk+MUUBe4pyMA4D1zEOJ1OHDx4EFdddZXnd1cceAXY+1fg/gNA4iTPjj0M\nGhsbe7dSBWJCGAB0dXVh+vTplvr6+jxKaRXXekYa3qCvgFAonJudnb2nsLAwJJC6v5wPpRSnT5+G\nQCBATk7OZWcweosDb+yrwftH+ESy/ji/vSTrZHuNymF2gAgIxDJx75cwWOjXRSQopWCcDGhXJ5KL\nP4SiaSdcQinqMm6BZuJNEIXKcFLtwrYiC6ranAiXiHDnrFSsnp2GuDDPLh2VlZUhPDwcycnJnhvU\nZQdeywXiJgCrtnpu3GFSW1uLjo4OzJgxI+D2OffAsiyWLFliLywsfECv17/PtR4uCAiDJoREA9h7\n7tcEAAyAns4mkwGUABABqAdwF6VUTwhJA1AB4CwAAsAM4G5K6dnzx05KSlq3dOnSR/75z38GpkPD\nfZKtrKyE2WxGXl7eFT/wjVoz1v/3LHacaUWUTIhfzpJhIZ9IdgEOkwOmDhOi0qMuuY91sXBYHHCY\nHHCYHWDsDIiQIEgShCBJEEQSEUTBIgjFvmXclFKwLhYumwtOq7P3i7rcofwgWRDEMjHCHS1QntiE\n8OZCaIMT8SKzEp+Z8pASJcU98zJw6/RkSMXeme3ZbDYcO3YM8+fP99xrd+ojYNuDwJ1fAWMXe2bM\nYUApRXl5OWw2G/Ly8vymFsNQePHFF11vvvnmf5uamm7gWgtXBIRBnw8h5GkAJkrpS+d+N1FK5ed+\n/gBAFaX0uXMG/Q2lNOfcffcDmEMpXX3ReILk5OTil19+Ofu2224L3E8D3KUBW1tbMWPGjH7Xs042\n6vDcjnKcatIjNUaEu+aGIi81YK9hBkV3SzdEwSJIo/veGnQxLMP2Gp7L6oLL7gLjcJcdFYqF7q+g\nC78LhAIIRAKPhMwpS8EyLFgXC8bJgHWyYByM+8vJwGV3AQAEQQKIgkUICglCkNR9QXFxBrvOzODb\nEgvMZ47gUfoRsgRqaGPzEbHs/yBUTBm21v4oLi5GYmKiZ1oqsizw1ixAKAYeOMB5ty+WZVFUVISQ\nkBBMnDjRpy7gPM3x48fxi1/8or2lpSWDUmrhWg9XBObCxeU5AuByi0hhAHQX30gpZQkh1zz66KNV\n+fn5YWlpad7Uxynp6ekIDg7GkSNHkJ+ff8Xs9WmpkfjyN3PwXWkb1n1Xgb9t1WGKUoy75oUiLSYw\nk1UGir3bDtmYge8RFggFCJYHI1h+4QUOpfQCo2QcDJxWJ1in20xZF4uLL7AJIW7TFgAEP53AKSjA\nus24z+eICARCgfsC4NxFgFgudv88gDB8k9aJbacsOFhlhYsBlmRfC+O8NUDHVkT/sA54dwEwZSWw\n+H+A0IQBvzaDZezYsSgpKfGMQVfvAjrPAr/YxLk5u1yu3hoGPYWGAhWj0YiVK1daWlpaFo9mcwZG\n0QyaECIE8AmAf1JK/3tRiDsUgBTATEppU1/jisXixbm5ud8cO3YsJFATMnro7OxEaWkppk6dirCw\n/qtgOVwsPjraiA17q9BtdWHhRAlunyVHlDww18auBMuw6DjbgfiJI99eklIK0L5NGDjPvAk8Mvui\nlOK0yoFtRRYUN9ohCRLg1ukp+PXcdKTFnHeBYtUDB14Cjr7jno3O+wMw+0FAPLAIw2ApLCzEmDFj\nEBV16RLDoHjv54CuAXi4GBByd9FptVpRWFiIjIwMz66v+yg33XST7dChQ/+vs7PzDa61cM1oMGgG\nwBkACrgNeSGllOkjxP1LuNegf3a5sRUKxevLli174M033wzMzYbnYTQacfLkSUyYMGHAsxGDxYk3\nvq/G+4cbICDATVOluHGqDBJxQK8MXEDPVqIIpe9VmvIUTobiUJUN24ssaOh0IkYuxq/mpGHlzFRE\nyq7w0eiqA3Y/BVRsA8IUwNVPAzm3AB5eR9XpdKiqqsLMmTOHPoj6JPDuIuDa54A5D3pO3CDR6/Uo\nKirCpEmT/LLE8GB57bXXmJdffvnH5ubmq2mgmdMQGA0G3TODlgLYCeBzSumGPgxaAkBLKb3sZT0h\nRKhQKMpef/31rJtvvjlwF4DO4XA4UFhYiISEBGRkZAx41tWktWD9zkp8c7oVkVIBVsySY+HE0ZFI\npm/SIyQ8BCHhgVXcBgDMdha7Sy3YUWxFl5lBZpwc9xZk4Ka8JASLBhEtaTgE7HzCXdtaMQ1Ysg5Q\nDsNM++Dw4cPIzc0dere5z1YDtfvcZT1DuKml3tLSgurqakyfPh0ymWfLqvoiJ06cwLJlyzrVanUG\npdTItR5fYNQY9Lmf8wBsATAGQDIuNOhrALxCKc3tZ/yk5OTkip07d4ZNnDjRi3+Jb8CyLEpKSkAI\nwaRJkwaVNXqqSYfndlTgZKMOymgRVs0NxZRUcUAnt7SXtSN2fKzftJccCJpuF3YUW7C3zAqrk2Lu\nmGjcMz8DC7Jih/5esixw+hNg7zOAsRXI/gVw9V+BPgqdDEmzRgO1Wo28vLzBP7mrHvj7VGDOQ8A1\nz3hEz2CglKKqqqpnD3DAFiA5n46ODsyePdtSW1s7h1JawrUeX2FUGfS537cD+AzAAVy4zcoB4EFK\n6bEBHGNmVlbW98eOHZNERARuKLMHSilqa2vR3t6OadOmDar0KaUUO8vasO67SjRqLZisFGPV3FCk\nxQbeSYdxMuiq7ULs+FiupXiEmnYntp0y40iNDQJCcMPkJNxTkI7spHDPHcRhBg5tAA69DlAWmP1b\nYN7aYc9aKaU4cOAAZsyYAYlEMrgn73gMOPk+8IczwAh07jofl8uF4uJiiMVi5OTkBPQ2qh5cLhfm\nz59vKy8v/7Ver/8P13p8iYAz6JEiOjr63tzc3A179+4NCdRCARej0WhQVlaGyZMnDzoBx+Fi8fHR\nRrx+XiLZillyRAdQIplFa4HL7kJYkp+2lwTAUoqT9XZsK7KgXO2APFiIlTNT8au5aUgMH6TRDQaD\nGtj3LFDyH0AWCyz8CzB11bAacajVauh0OuTkDKKPtVnrLuuZsxxY9taQjz0UTCYTTp48iYyMDKSk\n+Fa9b2/y61//2r5r166Nzc3ND3OtxdfgDXoYpKSkbL7pppvueOONN0bNBmCLxYKTJ09CoVAgPT19\n0CFOg8WJN3+owXuH6iEgwA1TpVgWIIlkXfVdkMfKIZb7Xw6h3UXxY4UV24staNG5kBQRgl/PTccv\nZ6QgNGQEox3qU8DOPwNNR4C4bGDJc8CYhUMaakitKH94EfjheeC3R93Vw0aIlpYWVFVVIS8vD+Hh\nHoxQ+DgbNmxwrV+/vlCtVs+jlLJc6/E1eIMeBoQQYXJycuFTTz01KVCbavQFwzAoLS2Fy+XC5MmT\nh1QHWNVlwfqdZ7G9pAURUgF+OUuOxX6cSHZ+eU9/WmM3WBj897QF/z1jRbeVRa4iHPfOz8B1OQkQ\ncbWOTilQvhXY/b+AvhHI+hlwzbNAbNaghxpUK0qnFXg1B1BMBVZ+PgThg4dlWVRWVsJoNCIvLy9g\nu1H1xZ49e7B69eqWlpaWcZRSE9d6fBHeoIcJISQ8KSmp6tNPP42bN28e13JGlKamJtTV1Q3rqr+o\nSYfnvq3AiQYdUqJEuGteKKb6YSKZ0+pEt7ob0WP9YyuMWufC9iIzfqiwwclQXD0hDvcWZCA/Pcp3\nXnunDTi+Edj/EuC0ANPXAAseB6QDX17paUU5f/78/mtWn9gMfPMIsPobIL1gmOL7x2Kx4NSpU4iN\njUVWVpbvvO4jQG1tLRYuXGhUqVSTKKUNXOvxVXiD9gCEkKzU1NRT+/fvlymVSq7ljChGoxFFRUVI\nTk4eUsgb6Ekka8e67yrQqLVgUooYq+aFIt2PEslM7SaAAPI4ef8P5ghKKcrVTmwrMuNEvR1iEcHN\nU1OwZl46xvqwbpg63GHnk+8DwWHAVX8CZtwDiAY226yqqkJQUBDS09Mv/yCWAd6Y4U5Ou/d7r1cO\n6wlp5+bmjor9zedjNBoxa9YsW2Vl5RKGYfZzrceX4Q3aQ4SEhPxs/PjxXx85ciRk0Fmjfg7DMCgv\nL4fFYsGUKVMw1M5fDheLfx9rxGt7q2GwOLFggrsiWXSo768edFZ3IiIlAqIQ36syx7AUR2ps2HbK\nglqNE5HSIKyanYa7ZqciRu5H6ROaCmDnX4DavUDUGODaZ909mvsxU6fTiUOHDmH+/PmXz4qu2A58\neidwy2Yg52YviHfjcrlQWloKp9OJyZMnj6qQNuAO6S9dutReVFT0uEajeY1rPb4Ob9AeJCEh4c+T\nJ0/+32+//TZ4tGR2n09bWxsqKiqQnZ2NuLi4IY9jsDrx1g812HzwXCJZnhTLpvluIhllKTTlvrf+\nbHWw2FNmxY5iCzqMDNJjpLinIAM3T01GSJAf/39W73YbdedZIK0AWPJ8v32a+21F+Y9rAFMb8FAR\nIPTORZZer0dJSQnS0tKgVCp96n9lpHjggQcc33zzzZfNzc13cK3FH+AN2sMoFIp3Fy9efNeHH37o\nR1MTz2Gz2VBSUoLg4GBkZ2cPq8iCqsuCl3adxdbic4lkM+VYnO17iWR2ox0WrQWRaZFcSwEAaI0M\ndpRYsLvUAouDIj89CvcVZGDR+DgIfOy1GzKM0x3y/v55wKoD8lYCiy7fiKOnnnVBQcGlxth0FNi8\nBFi6Hph5v8elsiyLqqoqdHZ2YvLkyUOvbubnPPPMM85NmzYdU6vVCyilDNd6/AHeoD0MIYQoFIod\nd9xxx+L169ePrvjVOSilUKlUqKurQ3Z2NmJjh1e4o0Slx992lKOwQYfkKBFWzZVjalqwz8xAutXd\nEElEkEZ5p/nDQKnvcBcWOVRtAwAszUnEvQUZmJwSwMV0rHpg//8Bxza6G3EUPOJuxBF06TJTcXEx\nkpKSLo3u/OcOoOmwu6yn2LMlNQ0GA0pKSpCYmIixY8f6zP/sSLNx40bXs88+W6VWq6dSSu1c6/EX\neIP2AoSQIIVCcXTt2rW5a9eu9Z9MJw9jtVpRXFwMmUyGiRMnDmk7Vg+UUuwqb8e6byvQoLUgN9md\nSJYRx/3Lq6nQIDozGsLB1KP2EJRSFDU6sL3IjNMqB6RiIVbMUOLuuWlI4fiCYUTR1gJ7nnKvJYcl\nuxtx5N5ywfq0yWRCSUkJ5s6d+9PzOqvdyWHzHwMWPekxOSzLoqamBu3t7Zg8efKAusIFKlu2bGF/\n97vftba0tGRTSg1c6/EneIP2EoQQWVJS0un169enrVy50jcXT0cASimamppQX1+P8ePHIyFheL2A\nnQyLfx9rwmt7qqC3OHHV+BDcPjsUMRwlkrEMi86znYibOPQ196HgdFHsP2vF9iILVF0uxIcF4+65\n6bg9X4lwCfcXLZzRcNBd6KS1BFBMB362DkjJ7737+PHjyMzMRGTkueWI7Q8DxbsUfkkAAB+ASURB\nVP8BHikF5J55D7u6unDmzBkkJCQgMzNzVJTrvBwHDx7EihUrtGq1OodS2sa1Hn+DN2gvQgiJTkpK\nKnvvvffirr322tEZ2zqHzWZDaWkpWJZFbm7u4OsjX0S3zYm3vq/F5kN1AIDrp0jxi2kySINH9mRo\n1VvhMDoQnjIy1Z+MVhY7Sy34rsQCvYXFhMRQ3FuQgesnJUEsGr1GcAEs6y4ZuvcZd+JX9nLgmr8C\nEUrodDpUV1cjPz8fMGnchUmm3A7c8PqwD+t0OlFeXg6z2YxJkyZBLvfhrWsjQFlZGZYuXWpUqVRT\nKaU1XOvxR3iD9jKEEKVCoSjZunVrxLRp07iWwzkajQbl5eVQKpVD3jd9Ps06C17aeRZbilsQLhHg\ntplyXJMzcolk+kY9QiJDEBLm3faSbXoXthdb8H25FXYXxVVZsbhvfgbmjIketeua/WI3AYc3uJtx\nUBaY/TugYC2qtr+OsY3/hsCodj/ummeBub8f8mEopb37mseOHYvk5ORR/56oVCpcddVVlvr6+vmU\n0pNc6/FXeIMeAQgh2ampqUd37dolz8oafLnCQMPlcvVmtWZnZ3ukUMPpZj2e21GBY/VdSI4U4c65\nckxP934iWXtZO+ImxIF46YKgstWBbafMOF5rh0hIsGyKAvcUZGBcwujMBB4Shmb3bPr0p4A4FNRl\nA2GdP90fJAFu2ABMum3QQ3d3d6O0tBQSiQTZ2dmjbl9zX2i1WhQUFNjq6upustlsu7jW48/wBj1C\nCIXCOenp6bt37dolzcjI4FqOT2AymVBWVgahUIiJEydCKh1eUhOlFHsqNFj3bQXqOs3ISRZjtRcT\nyVwOF3T1OsSO82x7SYalOF5nx/YiM862OhEuEeHOWalYPTsNcV6eqQc06pPA5p8BjOPS+8JT3OvQ\nA8ThcKCyshLd3d3Izs7+aU17lKPX67FgwQJ7Y2PjPTqd7mOu9fg7vEGPIGKxeEFqauq3e/bskaSm\neqYxfSDQE/ZOSEjA2LFjh5XtDbgTyT453oRXdldBd14iWayHE8nMnWawThahiZ6ZzdqcLPaVuwuL\ntBkYpERJcM+8DNw6PRlSse9VKPNLno4A0Nc5jwBP6/t9OsuyaGhoQGNjIzIzM6FQKEZ9OLsHg8GA\nRYsW2evq6n6r0+k2c60nEOANeoQRi8WL09LStu/du1cymnq+9sf5J7709HQolcphZ79225x454da\n/PNgPSioxxPJumq7IE+QQywbXlhTZ2bwbYkFu85YYbKzmKqMwH3zM3DNxASfK8ri97yaAxhUl97e\nzwyaUorW1lZUVVUhPj4emZmZw76QDCSMRmOPOT+s1Wo3cq0nUOANmgNCQkKWpKamfr1v3z6JQqHg\nWo5P4XQ6e/ePZmZmIikpadgzFLXeipd3nsVXRereRLKrsyUQCYc+rifaSzZpndheZMGBs1a4GGBJ\ndgLunZ+OaakD79bEM0hOfwZs/727tWQP/axBd3R0oLKyEmFhYRg3bhxCQvhlhvMxm81YtGiRvb6+\n/lGNRvMm13oCCd6gOUIul1+nUCi+2LNnDz+T7gObzYaqqiro9XqMHz8esbGxwzbqM80GPPdtOY7W\ndUERKcJdw0gkc1qc6G7tRvSYwSW4UUpxWuXA9iILihrtkAQJcOv0FPx6bjrSYjxbxYrnMpz+DNj7\nDKihGbbgGAQv/RsEU1Zc8jC9Xo+KigoEBQVh/Pjxo37bVF/0zJybmpr+1N7ePvy9ajwXwBs0h4SE\nhCxJSUn5evfu3ZK0tDSu5fgkZrMZlZWVsFqtyMzMRFzc8BpSUEqxt0KDdd9VoLbDjGyFuyLZ2PjB\nJZIZ24wQCAWQxQ7MVJ0MxaEqG7YXWdDQ6USMXIy756Zj5UwlIqR85i9X9NWKsqurC1VVVQCACRMm\nDLnXeaBjMBiwePFie0NDwyOdnZ1vc60nEOENmmPEYvFipVK5fefOnZIxY8ZwLcdnMZvNqKqqgslk\nwtixY5GQkDAso3YxLP5TqMKru8+iy+zE/HHuRLK4sIElknVUdSAyNRKi4CuvQ5rtLHaXWvBtiRVa\nE4PMODnunZ+Bm6YkIZiD0qA8F3J+K0qdToeqqioIhUJkZWUhIiKAa5gPE51O1zNzfkir1b7LtZ5A\nhTdoH0AsFi9ISUn59ptvvpFMmDCBazk+jcViQXV1NfR6PcaMGYOkpKRhJZMZbU6882Mt/nGgHiyl\n+PkUKZZPl0F2hUQyylJoKjSIz46/7GM03S7sKLZgb5kVVifF3DHRuHd+Bq7KGn6onsdzUEpRWFgI\nk8mEsLAwZGVljeq62QOhvb0d1157rV2lUv2mq6vrPa71BDK8QfsIQqFwVnJy8u7PP/9cnp+f3/8T\nRjlWqxV1dXXQaDRISUlBamrqsFpbtuiteGnXWXx1So0wiQC35stwbY60z0QyW7cNVp0VkamX7n2t\naXd3lDpSY4OAENwwOQn3FKQjO4kPk/oSDMOgubkZDQ0NkMvl6O7uxoIFC/iLp36oq6vD0qVLra2t\nrau7u7s/51pPoMMbtA9BCJmgUCgOvfvuuxFLly7lzxQDwOVyoampCU1NTYiOjkZGRgZksqEnW5Wq\nDXhuRwWO1GmRFCnCXXPkmJFxYSKZodkAsUwMSaS7njhLKU7W27GtyIJytQPyYCFWzkrFr+akITF8\neDXHeTyL3W5HfX09WltbkZSUhLS0NAQHB6OoqAgKheLSVpQ8vRQXF2PZsmVmlUp1HcMw+7nWMxrg\nDdrHIIQkJyUlFa5bty521apV/CLlAOnZp1pfXw+hUIi0tDTEx8cPaUZEKcX3ZzV4fkcFajrMmKhw\nVyRr0Tvxr8MmdBpZxMgFuG2WHAwDbC+2oEXnQlJECNbMy8AvZ6RA3s/aNM/IQSmFVqtFQ0MDzGYz\n0tLSkJycDKHwp4+X0WjEmTNnMGfOHA6V+i779u3D6tWrDc3NzQWU0jNc6xkt8AbtgxBCIhUKxfGH\nHnoo9U9/+tMo7h04NLq7u9HQ0ACtVoukpCQolcohdc9yMSw+PaHCK7uqoDU7ICAA28fHJVcRjvvm\nZ2BpTgJEQr6jlK/gcDigUqmgUqkQFhaGtLQ0REZGXvai7ZJWlDwAgM8++4xdu3atVq1W51NKG7jW\nM5rgDdpHIYRIFArFj7feeuvkl19+WTyae8oOFZfLhZaWFjQ2NkIsFiMlJQXx8fEXzJwGgsnuwuzn\n98Jod11yX4xcjMK/XM2vXfoIlFJ0dHRApVLBaDQiJSUFKSkpA2picUErSh4AwIYNG1wvvvhic0tL\nywxKaSfXekYbfBzOR6GUWgkhc7744ouv2trarv3444+DB2ssox2RSASlUgmlUonu7m6oVCqcPXsW\nUVFRSE5ORlRU1ICMVR4sgqkPcwYArcnBm7MPYDAY0NzcjPb2dkRHRyM9Pf2Ks+W+iIyMhNPphMlk\n4ouSAPjzn//s+PDDDytaWlrmUUpNXOsZjfAzaB+HEEKSkpLemDBhwpotW7YE8yeO4UEpRWdnJ1Qq\nFbq7uxEXF4ekpCSEh4df8WQ+94V9UOutl9yuiJDg0OOLvCmZ5zKYTCa0tLSgtbUVEomkN0IynGiT\nRqNBS0sLpkyZ4kGl/oXL5cI999zj2Lt374/Nzc0/p5Q6+38WjzfgDdpPiI2NfSQ+Pv75rVu3hvAF\nTTyDy+XqPSEbjUbExsYiKSmpz5nXliI1Hv/qNGxOtvc2SZAQ65bnYlkeX099JKCUwmg0oqWlBe3t\n7QgJCUFiYiISEhI81oeZUooDBw4gPz9/VNbc7urqwo033mivr6/f3NLS8iCllO3/WTzegjdoP0Is\nFi9MSEjY+t5774UuXryYazkBBcMw6OjoQEtLCwwGAyIjIxEfH4/Y2NjerkXv7i7Gu8fa0WFyISlC\ngv+3ZBxvzl6GYRhotVpoNBp0dnZCJpMhMTER8fHxw9r3fiWam5thMBiQnZ3tlfF9ldLSUixfvtza\n0dHxgE6n+5BrPTy8QfsdhJD0pKSkg48//njcQw89xOcQeAGWZaHT6dDe3o6Ojg6IRCLEx8dDo9Eg\nOzubr83sZSwWC9rb26HRaGC1WhEdHY34+HhER0cPOsFvKLAsi/3792Pu3LleuwjwNbZs2cI++OCD\n3Wq1+mpK6Umu9fC44Q3aDyGEyBUKxe4lS5bkbdy4MZjvS+tdrFYr2tvbUVZWBolEgrCwMERHRyMm\nJgZyuZxPEhsmFosFnZ2d6OzshMFggEQiQVxcHOLi4jhL1qqvr4fT6URWVhYnxx8pKKV49tlnnRs3\nblS1tLTMpZS2ca2J5yd4g/ZTCCEChULxmlKpvHf79u0h0dGDa3vIMzj0ej1qa2sxdepUGI1GdHZ2\nQqvV9mb8RkZGIjIyEuHh4eAvmC4Py7IwGAzQ6/XQ6XS9hhwTE4Po6GiEh4cPK8nLUzAMg/3792P+\n/PkjMmvnApvNhpUrV9qOHz/+fXNz8y8opXauNfFcCG/Qfk5ERMTK2NjYdz/77DNJXl4e13IClurq\nagQHB0OpVF5wO6UUJpMJOp2u13AopQgPD0dkZCTCwsIQGho6Kk2bYRiYTCZ0d3dDr9dDr9eDYZje\n1yYiIqLf7HkuOXv2LIKDgxGIrWCbmppw44032trb29e1tbU9S3kj8El4gw4ACCF5CoVi73PPPRe+\nevVq7qcfAcjhw4eRl5c3oIpkDMPAYDBAp9PBaDSiu7sbDMNAKpUiLCwMYWFhkMlkkMlkAbHG6XK5\nYLFYYDab0d3dDaPRCJPJBEIIQkNDERYWhvDwcERERPjV3+twOHD48GFcddVVPnsRMRR2796Ne++9\n19za2nqr3W7/jms9PJeHN+gAgRASo1Aodi1atGjipk2bgkfjFhFvwTAMDhw4gAULFgx5DEopL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FtMhDZGYbgC1mtmXjxo0vZrPZXbVabSiO4zX5fJ7NmzfHw8PDSalUsmKxyKZNmygUCv/LLmYh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\nItKBFNAiIiIdSAEtIiLSgRTQIiIiHehP3tZI80Nlz10AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b75cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(14,8))\n",
    "ax1=fig.add_subplot(1,1,1,polar=True) #设置第一个坐标轴为极坐标体系\n",
    "\n",
    "gsw=hou_and_gsw_data[0,:] #提取GSW的信息\n",
    "hou=hou_and_gsw_data[1,:] #提取HOU的信息\n",
    "label=np.array([j for j in hou_and_gsw_df.columns]) #提取标签\n",
    "\n",
    "angle = np.linspace(0, 2*np.pi, len(gsw), endpoint=False) #有几个label，就把整圆360°分成几份\n",
    "angles = np.concatenate((angle, [angle[0]])) #增加第一个angle到所有angle里，以实现闭合\n",
    "gsw = np.concatenate((gsw, [gsw[0]])) #增加gsw的第一项数据，以实现闭合\n",
    "hou = np.concatenate((hou, [hou[0]])) #增加hou的第一项数据，以实现闭合\n",
    "\n",
    "ax1.set_thetagrids(angles*180/np.pi, label, fontproperties=\"Microsoft Yahei\") #设置网格标签\n",
    "ax1.plot(angles,gsw,\"o-\",label='GSW')\n",
    "ax1.plot(angles,hou,\"o-\",label='HOU')\n",
    "ax1.set_theta_zero_location('NW') #设置极坐标0°位置\n",
    "ax1.set_rlim(0,1) #设置显示的极径范围\n",
    "ax1.fill(angles,gsw,facecolor='g', alpha=0.2) #填充颜色\n",
    "ax1.set_title(\"HOU VS GSW\",fontproperties=\"SimHei\",fontsize=16) #设置标题\n",
    "plt.legend(loc = 'best')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          0.6981317   1.3962634   2.0943951   2.7925268   3.4906585\n",
      "  4.1887902   4.88692191  5.58505361]\n"
     ]
    }
   ],
   "source": [
    "print(np.linspace(0, 2*np.pi, 9, endpoint=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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